From d0525cc4375b74724d18da45e584ff5b779a2f06 Mon Sep 17 00:00:00 2001 From: Dennis Thiessen Date: Sat, 18 Jul 2026 21:03:45 +0200 Subject: [PATCH] chore(applications): archive BIS + NATO submitted packages, ignore tmp/ Add BIS Basel and NATO JWC Stavanger application output (resume/CV, cover letter, session file, critique) plus the Bundeswehr experience file backing the NATO package; update session status tables in AGENTS.md/CLAUDE.md; record two new Bash allowlist entries for the job scout venv. tmp/ is scratch working files, now gitignored. Co-Authored-By: Claude Sonnet 5 --- .claude/settings.local.json | 4 +- .gitignore | 3 + AGENTS.md | 2 + CLAUDE.md | 2 + JDs/NATO_AI_ENGINEER.txt | 142 +++++++++ JDs/bis_senior_data_analytics_ai.txt | 57 ++++ .../critique_bis_senior_data_analytics_ai.md | 270 ++++++++++++++++++ ..._senior_data_analytics_ai_cover_letter.tex | 40 +++ ...2e_bis_senior_data_analytics_ai_resume.tex | 140 +++++++++ output/BIS/resume.cls | 199 +++++++++++++ .../session_bis_senior_data_analytics_ai.md | 155 ++++++++++ output/NATO_AI_ENGINEER/NATO_AI_ENGINEER.txt | 142 +++++++++ .../critique_nato_ai_engineer.md | 229 +++++++++++++++ .../e2e_nato_ai_engineer_cover_letter.tex | 42 +++ .../e2e_nato_ai_engineer_resume.tex | 136 +++++++++ .../session_nato_ai_engineer.md | 160 +++++++++++ .../experience/experience_bundeswehr.md | 24 ++ .../experience/experience_swisscom.md | 20 ++ 18 files changed, 1766 insertions(+), 1 deletion(-) create mode 100644 JDs/NATO_AI_ENGINEER.txt create mode 100644 JDs/bis_senior_data_analytics_ai.txt create mode 100644 output/BIS/critique_bis_senior_data_analytics_ai.md create mode 100644 output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex create mode 100644 output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex create mode 100644 output/BIS/resume.cls create mode 100644 output/BIS/session_bis_senior_data_analytics_ai.md create mode 100644 output/NATO_AI_ENGINEER/NATO_AI_ENGINEER.txt create mode 100644 output/NATO_AI_ENGINEER/critique_nato_ai_engineer.md create mode 100644 output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex create mode 100644 output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex create mode 100644 output/NATO_AI_ENGINEER/session_nato_ai_engineer.md create mode 100644 resume_builder/experience/experience_bundeswehr.md diff --git a/.claude/settings.local.json b/.claude/settings.local.json index 5ee7c88..723b73b 100644 --- a/.claude/settings.local.json +++ b/.claude/settings.local.json @@ -118,7 +118,9 @@ "WebFetch(domain:careers.cisco.com)", "WebFetch(domain:job.bkw.com)", "Bash(\"C:/Users/Dennis/AppData/Local/Programs/MiKTeX/miktex/bin/x64/pdflatex.exe\" -interaction=nonstopmode -output-directory=output/Kraken_SRE_AI_Agents output/Kraken_SRE_AI_Agents/e2e_kraken_sre_ai_agents_resume.tex)", - "Bash(python resume_builder/helpers/char_count.py -f resume output/Microsoft_ISE_Senior_SWE/e2e_microsoft_ise_resume.tex)" + "Bash(python resume_builder/helpers/char_count.py -f resume output/Microsoft_ISE_Senior_SWE/e2e_microsoft_ise_resume.tex)", + "Bash(python scout.py --help)", + "Bash(./.venv/Scripts/python.exe scout.py --hide-decided)" ] } } diff --git a/.gitignore b/.gitignore index ece8be9..b4af57c 100644 --- a/.gitignore +++ b/.gitignore @@ -46,3 +46,6 @@ job_scout/*_verify.txt # Session-specific (users may want to keep these — uncomment to ignore) # output/ + +# Scratch/temp working files +tmp/ diff --git a/AGENTS.md b/AGENTS.md index a66062d..ee3f28b 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -143,8 +143,10 @@ _Update this section when starting/finishing a JD._ | Session | Status | Next Command | |---------|--------|-------------| +| NATO JWC — Staff Officer (2030 Digitalisation – AI Engineer), Stavanger | **SUBMITTED 2026-07-10 via NTAP.** Compact 2-page resume, 1-page cover letter and portal responses submitted. Strong production ML/data reliability, limited domain-grounded LLM-agent configuration, German officer service and German nationality. Known gaps: 3+ years LLM ownership, fine-tuning/agents/hybrid search, Azure DS cert and current clearance. | Done — await response | | Kraken (Payward) — SRE, AI Agents (remote, CH-eligible) | **CLOSED — REJECTED 2026-06-17** (applied 2026-06-15 ~87.2/100, no interview). Honest gaps (NO Terraform/SRE-title/LangGraph) likely the filter; 4th Kraken req declined/rejected to date | Done | | Google — Senior Data Engineer (Merchant Data Science), Zürich/MV | **PASSED HIRING ASSESSMENT 2026-06-20 — Recruiting reviewing candidacy for next steps** (applied 2026-06-15, 85.5/100; cleared recruiter screen + assessment). Pass valid 24 months for future Google reqs. Possible additional role-knowledge OA may follow. Next: await recruiter outreach for interview scheduling; clarify L4/L5 + comp clears 180k+ when recruiter re-engages | Await recruiter next-step; prep for recruiter/tech screen | +| BIS — Senior Data & Analytics Engineer, Artificial Intelligence (Basel; 3-year fixed term) | **SENT 2026-07-10.** 2-page resume + 1-page cover letter; critique 80.5/100. Known ceiling: formal LLM evaluation, direct LLM ownership and banking domain; portal answers framed the LLM scope accurately. | Done — await response | | Snowflake — Sr SWE, Enterprise (Observe by Snowflake), Zürich | **SENT 2026-06-06** (~86/100; 2pp resume + 1pp CL; real Ashby JD; comp CHF 176–253k base; NO C++ gate). Tier 1+2 applied; Vizrt low-latency skipped per user. Best-fit role in the 2026-06 search | Done — await response | | Isovalent (Cisco) Sr Data Engineer, Observability | **CLOSED — role pulled** (live Cisco scrape 2026-06-02: not on board; Recruitee link dead). Package finalized ~86/100, SHELVED for reuse | Done — retarget PDFs to next live data-eng req (QuantCo/Grafana/Confluent) | | Google Zürich Sr SWE Infrastructure (Data Pipeline) | **CLOSED — DROPPED + DELETED 2026-06-02** (poor fit). Live JD = Core infra/systems SWE with **C++ as a MINIMUM qual**, off-thesis vs `user_positioning`. Output folder deleted (was built on a fabricated JD). | Done — do not reattempt this req | diff --git a/CLAUDE.md b/CLAUDE.md index e119388..21dee71 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -143,6 +143,8 @@ _Update this section when starting/finishing a JD._ | Session | Status | Next Command | |---------|--------|-------------| +| BIS Basel — Senior Data & Analytics Engineer, AI (jr100429, 3-yr term) | **SENT 2026-07-10** (80.5/100; deadline 2026-07-24; hybrid Basel, English-working intl org). Critique flags LLM-depth probe (integration/config vs. ownership) as the screening risk — prep honest answer before any call | Prep interview brief when screening lands | +| NATO JWC Stavanger — Staff Officer, AI Engineer (2030 Digitalisation, G15, 3-yr PLN) | **SENT 2026-07-10** (77.5/100; deadline 2026-08-09; final interviews 2nd half Oct 2026). Borderline screen: Azure-cert "or equivalent", LLM depth, no current clearance; German national + officer service are the legibility assets. Tax-free NOK 93,933/mo + allowances | Await screening; no action until contact | | Microsoft — Senior SWE, Industry Solutions Engineering (ISE), Zürich (req 200040836) | **SENT 2026-07-03** (85.8/100 Pass 2; 2pp resume + 1pp CL; verbatim Eightfold JD; IC4 base CHF 146.2–245.9k; applied ~7 days after posting). Same-day build→critique→submit. Decisions.json logged as applied | Done — await response | | Kraken (Payward) — SRE, AI Agents (remote, CH-eligible) | **CLOSED — REJECTED 2026-06-17** (applied 2026-06-15 ~87.2/100, no interview). Honest gaps (NO Terraform/SRE-title/LangGraph) likely the filter; 4th Kraken req declined/rejected to date | Done | | Google — Senior Data Engineer (Merchant Data Science), Zürich/MV | **PASSED HIRING ASSESSMENT 2026-06-20 — Recruiting reviewing candidacy for next steps** (applied 2026-06-15, 85.5/100; cleared recruiter screen + assessment). Pass valid 24 months for future Google reqs. Possible additional role-knowledge OA may follow. Next: await recruiter outreach for interview scheduling; clarify L4/L5 + comp clears 180k+ when recruiter re-engages | Await recruiter next-step; prep for recruiter/tech screen | diff --git a/JDs/NATO_AI_ENGINEER.txt b/JDs/NATO_AI_ENGINEER.txt new file mode 100644 index 0000000..4ed195e --- /dev/null +++ b/JDs/NATO_AI_ENGINEER.txt @@ -0,0 +1,142 @@ +Job Title: Staff Officer (2030 Digitalisation - Artificial Intelligence Engineer) +This vacancy notice is for a NATO-2030 agenda project-linked NATO International Civilian (PLN) post. +This post is limited to a three-year definite duration project. It will be filled as soon as possible. In view +of the urgency of this project, qualified candidates who hold or have recently held a valid NATO or +National security clearance will be given priority consideration. +Please note that the JWC is currently trialling a new organizational structure. Consequently, reporting +lines, job titles, functional alignments and some duties may differ slightly from those outlined in the +vacancy notice. +NATO Body: Joint Warfare Centre (JWC) +Primary Location: Stavanger, Norway +Schedule: Full-Time +Salary (Pay Basis): 93,933 NOK Monthly +Grade: G15 +Clearance Level: NATO Secret (NS) +Application Deadline: 9 August 2026 +The Joint Warfare Centre (JWC) is seeking a Staff Officer (2030 Digitalisation - Artificial Intelligence +Engineer) for its civilian workforce within the CIS Services Branch. +We are building a Data Science Team at NATO’s JWC in spectacular Stavanger, Norway. This team +will lead the transformation of JWC’s exercises to incorporate the latest emerging defence +technologies faster than ever before. The JWC’s AI and Automation initiatives will improve the mission +impact of AI for NATO operations and streamline business processes across the centre. +In this role, you will contribute as a key member of the JWC Data Science Team to deliver AI to NATO +exercises, gaining experience in multi-level security, international, and operational environments. You +will plan, develop, and deliver on a roadmap to bring cutting-edge AI (including latest generation LLMs, +reasoning models, and agentic systems) to operate on NATO’s strategic, data-centric exercises and +programs. Your hands-on experience will carry over as your team delivers and iterates with operators +from across the alliance. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +What the role offers: + High impact, portfolio-level work at the intersection of AI, defence, and NATO operations. + An English-language environment with colleagues from across the 32-nation alliance + An opportunity to live and work in one of Norway’s most beautiful coastal cities, with easy +access to the fjord and flights across Europe. + Tax free salary and privileges. +If you are ready for a challenging role that will leverage your technical skills and expose you +to an international cohort working on challenges at the highest strategic level, we encourage +you to apply. +Principal Duties +The incumbent's duties are: +• Provide subject-matter-expert advice, manage the development, refinement, and delivery of large +language model (LLM) artificial intelligence tools in support of JWC’s digital transformation effort. +• Highlight emerging technologies and opportunities for JWC to enhance its employment of its +digital transformation using machine learning in JWC process improvement. +• Deploy, optimize and manage large language model-powered applications in a JWC production +environment +• Fine-tune existing, pre-trained (LLM) to adapt to future JWC applications. +• Develop multi-modal solutions to address hybrid search +• Develop a roadmap for implementing Data initiatives and integrating NATO's digital +transformation into JWC’s processes and exercise production; +• Develop and apply validation methodologies to verify the accuracy and reliability of large data- +centric programmes, such as Geographic Intelligence and others across the JWC to ensure +cohesive LLM outputs in support of exercise delivery. +• Recommend, coordinate, and initiate projects aimed at sustainably embedding AI-based solutions +into JWC business processes. +• Collaborate with stakeholders, internal teams, and working groups, as needed. +• Provide expert advice and guidance on automation to the JWC’s leadership team and staff. +• Remain abreast of industry trends and best practices in artificial intelligence systems, contributing +to the continual enhancement of analytical processes and tools at JWC NATO. +Essential Qualifications +Education/Training +• University Degree in information technology, economics, statistics, operations research or related +discipline and 3 years function related experience, +• or Higher Secondary education and completed advanced vocational training in that discipline +leading to a professional qualification or professional accreditation with 4 years post related +experience. +Experience +• At least 3 years’ functional experience of developing, managing, and adapting artificial intelligence +systems including Large Language +• Models (LLMs). +• At least 2 year’s functional experience with Python, SQL and Spark or equivalent. +• Certification in Microsoft Azure Data Scientist or equivalent. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +Language +English – Upper Intermediate/Advanced +Desirable Qualifications +Professional Experience +• Problem solving skills: Strong analytical and critical thinking skills to identify patterns, trends and +outliers in data as well as being able to solve complex business problems using data-driven +approaches. +• Knowledge of and experience in programming, machine learning, data management, big data +technology, ethical considerations of data and project management. +Education/Training +• Master’s Degree or equivalent in artificial intelligence systems, computational science, or related +discipline. +• A recognized Project management Qualification (APM, Prince, AgilePM, etc.) +Personal Attributes/Competencies +• Considerable maturity and professional judgement is required to make decisions on to ensure +seamless provision of high-quality support to the JWC. +• High level of organisational and coordination skills. +• Excellent managerial, interpersonal, and communication skills with a visionary view to evolving +requirements to meet future challenges. +• Able to cope with stress and possessing good health. +• Must be able to work as a member of a team in a multi-national environment. +• An analytical, systematic and pro-active approach is important. +• Strong analytical and critical thinking skills to identify patterns, trends and outliers in data as well +as being able to solve complex business problems using data-driven approaches. +• A capacity for original thought, including the incorporation of emerging +• concepts. +• Self-motivated and capable of working under pressure. +Work Environment +The work is normally performed in an office environment. +NOTE: The work both oral and written in this post and in this headquarters as a whole is conducted +mainly in English. +Travel on temporary duty may be required for several conferences. +Irregular working hours may be required, especially during exercises/events +How to Apply for a Project Linked NATO Civilian Post at JWC: +JWC, as an equal opportunities employer, values diverse backgrounds and perspectives and is +committed to recruiting and retaining a diverse and talented workforce. We welcome applications from +nationals of all NATO Member States and strongly encourage women to apply. +Applications are to be submitted, in English, using the NATO Talent Acquisition Platform (NTAP) +(https://nato.taleo.net/careersection/2/jobsearch.ftl?lang-en). Applications submitted by other means +will not be accepted. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +NTAP allows for the adding of attachments. Candidates are to attach a copy of the +qualification(s)/certificate(s) covering the highest level of education and vocational qualifications held +to support their application. +Applications are automatically acknowledged within one working day after submission. In the absence +of an acknowledgement please make sure the submission process is completed or re-submit the +application. Applications will not be accepted after the deadline. +Remarks: + +Notes for candidates: The candidature of NATO redundant staff at grade G15/A-2 will be considered +before any other candidates. +Notes for NATO Civilian Human Resources Managers: if you have qualified redundant staff at +grade G15/A-2, who wish to be considered for this post, please advise JWC Civilian HR no later than +the closing date. +Final interviews are scheduled to take place during the second half of October 2026. +Contract: +This project post is limited to a definite duration of 3 years. There is no guarantee that this post will +continue beyond that period. Successful applicants will be offered a 3-year definite duration +employment contract. Serving staff will be offered a contract in accordance with the NATO Civilian +Personnel Regulations. +Salary: +Starting basic salary is NOK 93,933.00 per month (tax-free). Additional allowances may apply +depending on the personal circumstances of the successful candidate. For further details see NATO +Terms & Conditions. + +For any queries, please contact the Joint Warfare Centre Recruitment Team at +jwc.recruitment@nato.int \ No newline at end of file diff --git a/JDs/bis_senior_data_analytics_ai.txt b/JDs/bis_senior_data_analytics_ai.txt new file mode 100644 index 0000000..9a0559b --- /dev/null +++ b/JDs/bis_senior_data_analytics_ai.txt @@ -0,0 +1,57 @@ +Senior Data & Analytics Engineer – Artificial Intelligence + +Office location: Basel, Switzerland +Department: Architecture, Platforms and Intelligence +Application deadline: 24 Jul 2026 +Contract duration: 3 years + +Bank for International Settlements (BIS) is hiring a Senior Data & Analytics Engineer – Artificial Intelligence within the Architecture, Platforms & Intelligence team in Banking Technology. + +Application deadline: Please note that the deadline for applications is 24 July 2026 at the end of day. + +Please note that this role is offered as a three-year fixed term contract. + +This role is based in Basel, Switzerland; however, thanks to our status as an international organization, we can hire globally and welcome applications from candidates of all nationalities and located anywhere in the world. Relocation support is available for the successful candidate and their dependent family members. + +Purpose of the job: + +As a Senior Data & Analytics Engineer - AI, you will play a pivotal role in driving the organization’s data, analytics, and applied artificial intelligence capabilities forward. + +By analysing business requirements and translating them into effective solutions, you will contribute to the design and implementation of cutting-edge applications and services. + +By joining the Architecture, Platforms & Intelligence team in Banking Technology, you will: + +- Support the organization in the development, maintenance, and usage of data, analytics, and applied artificial intelligence IT applications. +- Join a collaborative team that supports the Bank’s ability to innovate and adapt in an evolving digital landscape. +- Benefit from working in a unique, international environment that offers flexible, hybrid working options with a blend of onsite work from our central office location in Basel and home office. + +Principal Accountabilities: + +- Collect and analyses business requirements, design and develop new applications or new features for existing applications, and provide production support for deployed applications. +- Design and implement testing and evaluation methodologies for AI applications, focusing on Large Language Model applications. +- Conduct data exploration and pre-processing to prepare large unstructured datasets for analysis, modelling, and use within AI applications. +- Stay updated with the latest AI trends, technologies, and best practices, and explore new tools and techniques to improve project outcomes. +- Develop APIs and interfaces for integrating AI models into existing systems and platforms. +- Monitor, maintain, and update models as needed to ensure they continue to meet business needs. +- Maintain regular communication with internal and external stakeholders to support operational activities. Assist junior staff by providing task-specific advice and support. Participate in external events under the direction of senior staff to represent the BIS. +- Collaborate with financial and business teams to understand the organization's financial structure and requirements. +- Develop own capabilities by participating in assessment and development planning activities as well as formal and informal training and coaching. Gain or maintain external professional accreditation, where relevant, to improve performance and fulfil personal potential. Maintain an understanding of relevant technology, external regulation, and industry best practices through ongoing education, attending conferences, and reading specialist media. + +Qualifications, skills and experience: + +- A degree in a relevant field (e.g., Computer Science, Data Science, Engineering, or a related discipline) or hands-on experience within this related field. +- A proven track record of designing, building, and optimizing cutting-edge AI solutions. +- Hands-on experience with data pipelines, analytical models, and data engineering tools and solutions, especially in the context of preparing data for AI use cases. +- Experienced with programming languages (Python). +- Exposure to financial / banking environment, including treasury, asset management, or related applications – would be nice to have. +- Experience with mentoring more junior colleagues – would be nice to have. + +Please note that the BIS's corporate language is English, which is used for all internal and external communication. + +Who we are: + +Internationalism is at the core of our identity and the best representation of this is our workforce. With employees from over 66 countries and offices in nine locations, the BIS is a global organisation with a truly international workforce. By joining us, you will work in a unique, highly rewarding, and international work environment. We are committed to equal opportunities at the BIS and aim to build a workforce that reflects our global membership. We strive to attract the best talent and foster an inclusive environment. We welcome applications from all qualified candidates, including those with a breadth of professional experience. + +What the BIS offers: + +We want your time at BIS to be a rewarding and career-enriching experience. We offer an agile and flexible working environment with hybrid working opportunities including home office and working from abroad days. To support our international applicants, we offer relocation support that extends to your dependent family members. In addition, we offer a competitive compensation and benefits package, including support for working families including childcare and education allowances (where applicable). Finally, we offer a genuinely unique international working environment that will give you exposure to the global financial system and the opportunity to collaborate with passionate experts from all over the world. diff --git a/output/BIS/critique_bis_senior_data_analytics_ai.md b/output/BIS/critique_bis_senior_data_analytics_ai.md new file mode 100644 index 0000000..bb3cccd --- /dev/null +++ b/output/BIS/critique_bis_senior_data_analytics_ai.md @@ -0,0 +1,270 @@ +# Critique: Bank for International Settlements - Senior Data & Analytics Engineer - Artificial Intelligence + +**Resume:** `output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex` +**Cover letter:** `output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex` +**JD source:** `JDs/bis_senior_data_analytics_ai.txt` - live BIS careers scrape, 10 Jul 2026 +**Date:** 10 Jul 2026 + +--- + +## 1. Domain-Specialist Lens + +### Reviewer persona + +The likely first technical reader is a senior applied-AI or data-platform engineer in BIS Banking Technology's Architecture, Platforms and Intelligence team. They run reliable internal applications in a risk-sensitive international institution, translate business needs into services, and will have reviewed candidates with direct RAG/LLM, financial-data and model-lifecycle experience. They will discount generic “AI” language, but production ML operating experience in a 24/7 fab is a distinctive signal. + +### Company context + +BIS supports central banks and financial stability through banking services, research and technology. This is an applied engineering role in Banking Technology, not an academic ML role: dependable data, controlled integration, operational support, security and clear communication matter as much as model capability. Project Gaia and Project Voyager make LLM-enabled analysis of unstructured information a credible near-term context. + +### JD vocabulary extraction + +| # | JD term | Importance | Meaning here | Resume match | +|---|---|---|---|---| +| 1 | AI application testing and evaluation | Highest | Repeatable evidence that LLM applications behave as intended | No | +| 2 | Data pipelines / engineering for AI | Highest | Reliable, governed preparation and supply of data to AI applications | Yes | +| 3 | Python | Highest | Day-to-day application and data engineering | Yes | +| 4 | Unstructured-data exploration / preprocessing | High | Preparing text, documents or other raw content for AI use | Partial | +| 5 | APIs / model integration | High | Connecting AI capability safely to existing systems | Partial-to-yes | +| 6 | Monitor, maintain and update models | High | Production ownership beyond deployment | Partial | +| 7 | Applied AI applications / production support | High | Business-facing services that must keep operating | Yes | +| 8 | Requirements and stakeholders | Medium | Turning business needs into maintainable applications | Yes | +| 9 | Financial / banking environment | Nice to have | Financial structure, treasury or asset-management context | No | +| 10 | Junior support / mentoring | Nice to have | Task-specific support and coaching | Partial | + +### Domain vocabulary map + +| Resume currently says | BIS-oriented reading | Why | +|---|---|---| +| Governed data products and metadata | Governed, discoverable data for AI applications | Closely maps data work to the role without claiming LLM ownership. | +| Production ML inference | Applied-AI application operated in production | Shows operational discipline relevant to the Bank. | +| REST APIs / data services | Interfaces for integrating AI-enabled services | Truthful bridge, but not evidence of owning a model-integration platform. | +| Grafana / Prometheus / ELK | Observability and production support | Maps to maintaining deployed systems, not model retraining. | +| ARTUS NLP / speech recognition | Unstructured audio and NLP bridge | Relevant adjacent experience; it must remain a contribution, not LLM expertise. | + +### Gap ranking + +- **Functional screen risk:** Formal LLM testing/evaluation methodology. This is a stated principal accountability and cannot be filled by the current evidence. +- **Serious competitive gaps:** Direct LLM-application ownership; large unstructured-document preprocessing; model updating/lifecycle ownership; banking or financial-data experience. +- **Cosmetic gaps:** Explicit mentoring evidence and external representation. The Bosch training and TIBCO co-presentation are helpful but do not establish a mentoring record. + +### Methodology transfer test + +| Resume achievement | How a BIS reviewer can map it | +|---|---| +| Bosch containerised ML inference | Evidence that Dennis can deploy an ML-dependent application under strict availability constraints; the missing piece is LLM-specific evaluation. | +| Swisscom governed data products and metadata | A credible foundation for controlled, discoverable data used by downstream AI applications. | +| Swisscom Python/Kafka ETL ownership | Direct evidence of Python, data quality, incidents and SLA ownership for data services. | +| Bosch application ownership and observability | Relevant to operating AI-adjacent applications and stakeholder support, though not proof of model updating. | +| Fraunhofer ARTUS and MISSION | A truthful bridge from NLP and API integration to LLM-enabled applications, without overstating depth. | + +### Competitive landscape + +- **Obvious fit candidate:** An LLM/RAG engineer from a bank, consultancy or regulated enterprise with evaluation harnesses, document pipelines, guardrails and model monitoring. +- **Dennis's advantage:** Genuine deployment and operations of ML in a 24/7 industrial environment, current governed cloud data-platform work, and clear Python/data-pipeline ownership. +- **Their advantage:** Direct financial-services context plus formal LLM evaluation and lifecycle experience. + +--- + +## 2. Five-Perspective Read-Through + +### ATS robot + +| Keyword / phrase | Resume result | +|---|---| +| Python | Exact | +| Data pipelines / ETL | Exact | +| Data engineering | Exact | +| Analytics / data analysis | Exact / semantic | +| Applied AI / ML | Exact | +| LLM applications | Partial: LiteLLM and custom GPTs, no application ownership | +| LLM testing / evaluation | Absent | +| Unstructured data preprocessing | Partial: image classification and speech transcription, no document-data claim | +| APIs / interfaces | Exact | +| Model integration | Partial | +| Model monitoring / maintenance | Partial: observability and production support | +| Production support | Exact | +| Business requirements | Partial: stakeholder and product-owner work | +| Stakeholders | Exact | +| Financial / banking | Absent | +| Mentoring junior staff | Partial: user training and colleague training | +| Docker / Kubernetes | Exact | +| AWS / cloud data platform | Exact | +| Data governance / quality | Exact | +| English | Exact | + +**Match rate:** 16/20 exact or defensible semantic matches (80%). The four gaps are concentrated in the most differentiating LLM/financial requirements, so the numerical match overstates the practical fit somewhat. + +### Recruiter glance (10 seconds) + +**Verdict: Forward.** Current Staff Data & AI Engineer at Swisscom, 11+ years, Python/Kubernetes/AWS and production ML are immediately credible for a senior hybrid data/AI role; direct LLM depth is not visible in the first glance. + +### HR screen (30 seconds) + +**Verdict: Phone screen.** The summary and skills clear the degree, Python, data-engineering and applied-AI requirements. HR may use the mandatory portal answers to clarify that LLM integration is real but limited and that formal LLM evaluation is not yet a proven responsibility. + +### Hiring manager read (2 minutes) + +**Verdict: Maybe interview.** + +1. Bosch production ML is the strongest differentiator and gives the application operational credibility. +2. Swisscom makes the data-platform and production-support fit clear. +3. The hiring manager will quickly test the gap between LiteLLM/custom GPT exposure and the role's request for LLM testing, evaluation, integration and model maintenance. + +**Predicted first question:** “Describe how you would design an evaluation and monitoring approach for an LLM application that extracts information from unstructured documents.” + +### Deep technical reviewer (10 minutes) + +**Truthfulness: clean, with one documentation correction already respected.** The 2025/26 Security Champion wording follows `config.md`, which overrides outdated three-year wording in the historical Swisscom experience file. The package does not claim LangChain, RAG, direct LLM application ownership, banking experience, formal LLM evaluation, or model retraining. + +| Claim family | Result | Evidence source | +|---|---|---| +| Swisscom AWS data products, Python/Kubernetes, Kafka/ETL, PySpark | Verified | `resume_builder/experience/experience_swisscom.md` (SW-2, SW-3, SW-6, SW-7) | +| Bosch production ML, data services, application ownership, Spotfire | Verified | `resume_builder/experience/experience_bosch.md` (BS-1, BS-2, BS-3, BS-5) | +| Fraunhofer ARTUS, MISSION and Jenkins | Verified and appropriately hedged | `resume_builder/experience/experience_fraunhofer.md` (FC-1, FC-2, FC-3) | +| Vizrt and Generali Python/quality/integration work | Verified | `experience_vizrt.md`, `experience_generali.md` | +| LiteLLM, custom GPTs, Kiro and Copilot | Verified scope: created/used LiteLLM APIs and custom GPTs; no framework substitution | `config.md` corrections log | + +**Consistency:** Resume and cover letter agree on Bosch ML, Swisscom pipeline ownership, Fraunhofer NLP, and the LLM boundary. No inflated company-wide ownership or unsupported publication claim appears. + +--- + +## 3. Eight-Dimension Scoring + +| Dimension | Score | Weight | Weighted | Notes | +|---|---:|---:|---:|---| +| ATS keyword match | 8.0/10 | 15% | 1.20 | 80% coverage, but the missing terms are high-value. | +| Summary | 8.4/10 | 10% | 0.84 | Strong production/data bridge; LLM limitation is necessarily outside the pitch. | +| Skills section | 8.0/10 | 10% | 0.80 | Specific and accurate. LiteLLM scope could be made more explicit. | +| Bullet quality | 8.0/10 | 25% | 2.00 | Strong evidence; limited quantified outcomes and no LLM-evaluation evidence. | +| Publications / research selection | 8.0/10 | 10% | 0.80 | No publication section is appropriate for this engineering role; Fraunhofer research is used proportionately. | +| Narrative coherence | 8.4/10 | 15% | 1.26 | Clear infrastructure-first applied-AI narrative from Bosch to Swisscom. | +| Page fill and visual | 6.5/10 | 5% | 0.33 | Clean two-page rendering, but Bosch is split across pages and page 2 has visible unused space. | +| Credibility signals | 8.2/10 | 10% | 0.82 | Swisscom, Bosch production ML, Application Owner and AWS certification are credible senior signals. | +| **Total** | | **100%** | **8.05 / 10 = 80.5 / 100** | Strong, with structural background gaps. | + +--- + +## 4. Interview Likelihood + +| Reader | Probability | Key factor | +|---|---:|---| +| ATS | 70% | Strong general data/AI terms, but evaluation and banking terms are absent. | +| Recruiter (10s) | 75% | Senior Swisscom title and production ML are easy to understand. | +| HR (30s) | 70% | Clears core engineering requirements; portal answers need to frame LLM scope carefully. | +| Hiring manager (2m) | 50% | Production engineering is strong, but direct LLM evaluation is the decisive gap. | +| Technical panel (10m) | 55% | Credible engineering depth if invited; expect rigorous LLM-evaluation questions. | + +| Ceiling scenario | Estimated score | +|---|---:| +| Current package | 80.5 | +| With the recommended scope clarification and visual improvement | 82.0 | +| Best possible truthful package today | 83.0 | +| Structural ceiling without direct LLM evaluation / banking experience | 84.0 | + +The ceiling is driven by background rather than resume quality. Do not try to close it with invented terminology. + +--- + +## 5. Tiered Improvements + +### Tier 1 - high impact + +1. **Improve the page-two continuation and fill** - **+1.0 to 1.5 points.** The Bosch heading and first bullet land on page 1 while the remaining Bosch bullets start page 2, which interrupts the strongest evidence block. If the two-line-only preference can be relaxed, expand selected page-2 Bosch/Fraunhofer bullets using already verified details; otherwise accept the sparse page rather than adding unsupported claims. This needs an intentional layout edit, not a wording-only tweak. +2. **Make LiteLLM integration scope exact in the skills line** - **+1.0 point.** Replace `LiteLLM API gateway` with `LiteLLM API gateway (created and used APIs)` if it fits the line. This adds truthful evidence for the portal's integration question without implying LLM evaluation or ownership. + +### Tier 2 - optional + +1. Add a precise AI-application phrase to the summary, for example: `I bring Python, data-pipeline, API and production-support experience for AI applications.` This improves exact JD-language matching without changing the claim. **+0.5 points.** +2. In the cover letter, replace the broad phrase `AI-adjacent data systems` with `data and ML systems` to reinforce the real Bosch signal. **+0.3 points.** + +### Tier 3 - cosmetic / skip + +1. Add more quantification only if a source provides it. The current 24/7 and 300mm context is useful; invented percentage impacts would damage credibility. +2. Add a banking keyword solely for ATS. This would be misleading and should not be done. + +**Verdict:** Apply the two Tier 1 changes if you want the strongest possible presentation before submission. Do not modify the package to imitate direct LLM-evaluation or banking experience. + +--- + +## 6. Interview Bridge Points + +| Resume topic | BIS equivalent | Opening line for interview | +|---|---|---| +| Bosch production ML inference | Operating an AI application in a constrained environment | “At Bosch, the key lesson was that model inference only creates value when deployment, availability and support work alongside the live operation.” | +| Swisscom data products and metadata | Governed AI-ready information foundation | “At Swisscom, I work on the part that makes downstream AI usable: data products have to be discoverable, governed and dependable before an application can rely on them.” | +| Python/Kafka ETL ownership | Reliable data preparation and production support | “I own Python/Kafka pipelines with data-quality and on-call accountability, so I approach AI inputs as production dependencies rather than one-off datasets.” | +| Bosch application ownership and observability | Maintaining deployed services | “Application ownership taught me to pair delivery with SLOs, user communication and monitoring; I would apply that same discipline to AI-enabled applications.” | +| ARTUS NLP contribution | Bridge to unstructured information | “ARTUS gave me hands-on experience contributing NLP and speech-recognition components; I would be transparent that my next growth step is formal LLM evaluation.” | +| MISSION REST microservices | Integration interfaces | “I have built containerised REST services for data exchange, which is the integration discipline I would bring when connecting model-enabled capabilities to existing systems.” | + +--- + +## 7. Cover Letter Critique + +### 7A. Anti-patterns + +- Pass: opens with a specific BIS Project Gaia reference, not a generic application sentence. +- Pass: strongest relevant evidence, Bosch production ML, appears in paragraph 2 and is prepared in paragraph 1. +- Pass: no defensive or apologetic background-gap wording. +- Pass: active close invites a conversation. +- Pass: no AI-fingerprint banned words or generic-opener phrases found. +- Partial: it uses a prose summary of Bosch/Swisscom/Fraunhofer evidence, but adds the required motivation and institution context rather than merely copying bullets. + +### 7B. Tailoring and context + +- Pass: names Project Gaia and Project Voyager. +- Pass: uses applied AI, LLMs, unstructured disclosures, data, governance, integration and maintenance vocabulary. +- Pass: accurately treats BIS as an international financial institution and Banking Technology as an applied engineering setting. +- Pass: connects governed data and production ML operations to the Bank's needs. + +### 7C. Industry-specific checks + +- Pass: business and operational value is clear through dependable data, production operation and 24/7 manufacturing context. +- Pass: jargon remains readable for a recruiter. +- Partial: it has no quantified business outcome beyond the 24/7 operating context; do not fabricate one. + +### 7D. CL ATS check + +The letter covers 7/10 priority terms or close variants: AI/ML, LLMs, unstructured data, data, APIs/data services, production operation/maintenance, and Python/Kafka pipelines. It does not claim testing/evaluation methodology, model updates, financial experience or mentoring. + +### 7E. Structural checks + +- Pass: 281 words, one page, correctly addressed, consistent date and contact details. +- Pass: claims trace back to resume bullets or the verified BIS project context. +- Pass: three varied paragraphs and no generic opener. +- Partial: one 24/7 operating context is the only quantitative signal, but that is preferable to unsupported impact numbers. + +### 7F. Package cohesion + +The resume stands alone for production ML and data engineering. The cover letter deepens why BIS through Gaia and Voyager, without introducing untraceable personal achievements. There are no contradictions. Total package length is three pages. + +--- + +## 8. Post-Generation Verification + +### Mechanical + +- Pass: all 20 variable experience bullets are within the two-line character target; the five short certification entries are fixed content. +- Pass: no overfull or underfull box warnings in the successful resume-check or cover-letter compilation. +- Pass: resume compiles to two pages; cover letter compiles to one page. +- Pass: all multi-line bullets have usable final-line fill; none ends in an `-ing` analysis phrase. The ARTUS bullet ends in the noun “setting”, not an analysis construction. +- **Needs improvement:** the resume's Bosch block is split across pages and page 2 has more unused space than the stated page-fill preference allows. + +### Content + +- Pass: 80% exact or defensible semantic ATS coverage. +- Pass: configured email is `dennis@thiessen.io` throughout. +- Pass: provenance and verb discipline are correct; Fraunhofer contribution verbs are hedged and Swisscom scope is not company-wide ownership. +- Pass: no LangChain, LangGraph, LlamaIndex, banking, direct LLM-evaluation, or model-retraining claim. +- Pass: cover-letter claims are traceable to the resume or verified BIS project information. + +### Structural and AI-fingerprint scan + +- Pass: Bank for International Settlements and Banking Technology are spelled consistently. +- Pass: both `.tex` files have standalone preambles and compiled successfully. +- Pass: employment dates use a consistent month-year format. +- Pass: no Tier-1 banned words, banned generic phrases or `---` em-dash tokens were found in either document. +- Pass: cover letter has varied paragraph openings and no generic opener. + +*End of critique.* diff --git a/output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex b/output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex new file mode 100644 index 0000000..2e4336e --- /dev/null +++ b/output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex @@ -0,0 +1,40 @@ +\documentclass[11pt,a4paper,roman]{moderncv} +\usepackage[english]{babel} +\moderncvstyle{classic} +\moderncvcolor{green} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{ragged2e} +\usepackage[scale=0.80]{geometry} +\usepackage[version=4,arrows=pgf-filled]{mhchem} +\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par} +\microtypesetup{expansion=false} + +\name{Dennis}{Thiessen, M.Eng.} +\address{Bern, Switzerland}{}{} +\phone[mobile]{+41~795~955~585} +\email{dennis@thiessen.io} +\extrainfo{\href{https://linkedin.com/in/dennis-thiessen}{linkedin.com/in/dennis-thiessen}} + +\begin{document} + +\recipient{Hiring Committee}{Architecture, Platforms and Intelligence\\Bank for International Settlements\\Basel, Switzerland} +\date{\today} +\opening{Dear Members of the Hiring Committee,} +\makelettertitle + +\begin{justify} +BIS's Project Gaia caught my attention because it applies LLMs to a real data problem: extracting usable climate-risk indicators from unstructured corporate disclosures despite differing reporting frameworks. That emphasis on dependable data, explicit technical choices and useful applications for central banks fits the work I do today. As a Staff Data, Analytics \& AI Engineer at Swisscom, I build governed data products and metadata on AWS that make enterprise information discoverable for analytics and AI. I am applying for the Senior Data \& Analytics Engineer -- Artificial Intelligence role to bring that production mindset to Banking Technology. + +My strongest production ML experience came at Bosch Semiconductor in Dresden. I designed and deployed Docker- and Kubernetes-based ML inference for automated image classification in a 24/7 wafer fab, where delivery had to operate alongside live manufacturing. I also built data services over OracleDB and Hadoop/ImpalaSQL for analysis applications, held Application Owner responsibility, and added observability through an ELK/Kafka proof of concept with Grafana and Prometheus. At Swisscom, I pair that operating discipline with Component Owner responsibility for business-critical Python/Kafka pipelines and their data quality, governance, incident response and on-call SLA. + +Earlier, at Fraunhofer CML, I contributed ML and NLP components to ARTUS, an automatic sea-rescue transcription project, and built containerized REST microservices for maritime data exchange. This gives me a practical bridge from NLP research to the LLM-enabled application work BIS is pursuing through Project Voyager. I would welcome a conversation about how my experience deploying and operating AI-adjacent data systems can support your team as it evaluates, integrates and maintains applications for the Bank. +\end{justify} + +\vspace{0.3cm} +{Sincerely,\\ +Dennis Thiessen, M.Eng.\\ +Staff Data, Analytics \& AI Engineer\\ +Swisscom (Schweiz) AG} + +\end{document} diff --git a/output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex b/output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex new file mode 100644 index 0000000..bf7d192 --- /dev/null +++ b/output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex @@ -0,0 +1,140 @@ +\documentclass{resume} +\usepackage{hyperref} +\usepackage{enumitem} +\usepackage{fontawesome} +\usepackage{tikz} +\usepackage{graphicx} +\hypersetup{ + colorlinks = true, + linkcolor = [rgb]{0.9,0.4,0.4}, + anchorcolor = [rgb]{0.9,0.4,0.4}, + citecolor = [rgb]{0.4,0.4,0.4}, + filecolor = [rgb]{0.4,0.4,0.4}, + urlcolor = [rgb]{0.0,0.0,0.99}, +} +\usepackage{xcolor} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{lmodern} +\usepackage[version=4,arrows=pgf-filled]{mhchem} +\usepackage[includefoot,left=0.5in,top=0.5in,right=0.5in,bottom=0.2in,textwidth=7.5in,textheight=10.8in]{geometry} +\usepackage{fancyhdr} +\pagestyle{fancy} +\fancyhf{} +\renewcommand{\headrulewidth}{0pt} +\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}} +\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}} +\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}} + +\name{Dennis Thiessen, M.Eng.} +\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}} +\address{dennis@thiessen.io \\ +41 795 955 585} +\address{Bern, Switzerland $\vert$ Open to Basel hybrid work} +\address{{Senior Data \& AI Engineer $\vert$ Production ML, Data Platforms \& Applied NLP}} + +\begin{document} + +\vspace{-0.15cm} + +\begin{rSection}{Summary} +Senior data \& AI engineer with 11+ years delivering software across telecom, manufacturing and applied research. At Swisscom I build governed data products and metadata on \textbf{AWS} for dependable analytics and AI, while operating \textbf{Python} services on \textbf{Kubernetes}. At Bosch I designed and deployed containerised \textbf{ML} inference for image classification in a 24/7 semiconductor fab; at Fraunhofer I contributed NLP for sea-rescue transcription. I bring Python, data-pipeline, API and production-support experience, backed by AWS Solutions Architect certification. +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Technical Skills} + +\begin{skillgroup}{Applied AI \& Production ML} +\skilldash{\textbf{Python}, production \textbf{ML} inference deployment, MLOps, image classification, Pandas, NumPy} +\skilldash{\textbf{Docker}, \textbf{Kubernetes}, Ansible, containerised deployment, GitLab CI/CD, Jenkins} +\skilldash{Applied NLP and speech recognition (Fraunhofer ARTUS contributor); PyTorch, Scikit-learn} +\skilldash{\textbf{LiteLLM} API gateway, custom GPTs with domain knowledge, prompt engineering, Kiro, Copilot} +\end{skillgroup} + +\begin{skillgroup}{Data Engineering \& Analytics} +\skilldash{ETL/ELT design and operations, \textbf{Apache Kafka}, \textbf{Apache Airflow}, PySpark / Spark, SQL} +\skilldash{Data products, metadata management, data governance, data quality, data modelling, analytical services} +\skilldash{Oracle, Teradata, Hadoop/Impala, Athena, Redshift, MS SQL; query tuning and data warehousing} +\end{skillgroup} + +\begin{skillgroup}{Cloud, APIs \& Operations} +\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, Step Functions, CloudWatch), SAA-certified} +\skilldash{REST APIs, FastAPI, Flask, Express.js; CloudFormation, serverless and event-driven architecture} +\end{skillgroup} + +\begin{skillgroup}{Observability \& Engineering Quality} +\skilldash{\textbf{Grafana}, \textbf{Prometheus}, Loki, ELK; alerting, incident response and production support} +\skilldash{pytest, BDD/Serenity, Selenium, CI/CD quality gates, code review and test automation} +\end{skillgroup} + +\begin{skillgroup}{Certifications} +\skilldash{\textbf{AWS Certified Solutions Architect -- Associate} (active to Sep 2027), Data Engineering with AWS} +\skilldash{iSAQB CPSA -- Foundation, IBM AI Engineering Specialization, AI for Trading Nanodegree} +\end{skillgroup} + +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Professional Experience} + +\begin{rSubsection}{Governed Data Products, Metadata \& AI-Ready Platform Operations}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland} +\item Build governed data products and manage metadata within Swisscom's Data Mesh on \textbf{AWS} (Glue, Athena, CloudFormation), creating dependable, discoverable sources for analytics and downstream AI workloads. +\item Design, deploy and operate \textbf{Python} data applications on \textbf{Kubernetes} with \textbf{GitLab CI/CD}, owning containerised delivery from build and test through production rollout and operation in an agile DevOps team. +\item Migrated my domains' Oracle/Teradata \textbf{ETL} to Swisscom's \textbf{AWS} platform (Glue, Athena/Iceberg, Redshift, \textbf{Airflow}), reducing manual operations with reliable, scalable serverless processing for analytics. +\item Own business-critical Fulfillment \textbf{ETL} pipelines from Oracle and \textbf{Kafka} to Teradata in \textbf{Python}, accountable for data quality, governance, incident response and restoration under an on-call SLA. +\item Deliver stakeholder data products, analyses and dashboards; partner with product owners on backlog priorities, automate recurring work and investigate production issues through 2nd/3rd-level support. +\item Apply \textbf{PySpark} for distributed processing in the Swisscom Data Lake, extending Python and SQL pipelines to large-scale batch workloads across Fulfillment and Product Analysis business domains. +\item Serve as Swisscom's 2025/26 Security Champion, the team's security point of contact, applying secure-development and risk-management training to enterprise data-pipeline delivery and operations. +\end{rSubsection} + +\begin{rSubsection}{Production ML Deployment, Data Services \& Application Ownership}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany} +\item Containerized and orchestrated \textbf{ML} inference with \textbf{Docker}, \textbf{Kubernetes} and Ansible in Bosch's 24/7 semiconductor fab, running automated image-based defect classification on 300mm wafer production lines. +\item Built data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis applications with structured process and defect data for 24/7 manufacturing operations. +\item Served as Application Owner for semiconductor analytics applications and pipelines, defining SLOs, training users, maintaining documentation and coordinating stakeholders for dependable 24/7 operations. +\item Co-owned Bosch's TIBCO Spotfire analytics platform for semiconductor engineers, developing C\# extensions and wafer-map visualisations; co-presented this work at the TIBCO Analytics Forum 2022. +\item Delivered an anomaly-detection proof of concept using ELK and \textbf{Kafka} on \textbf{Docker}, adding \textbf{Grafana}, \textbf{Prometheus} and Loki monitoring to validate centralised monitoring and alerting for manufacturing systems. +\end{rSubsection} + +\begin{rSubsection}{Applied NLP Research, API Integration \& CI/CD Automation}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany} +\item Contributed \textbf{ML} and NLP components to ARTUS, a Fraunhofer research project for automatic transcription of sea-rescue communications, applying speech recognition in a safety-critical maritime setting. +\item Contributed to a Fraunhofer research-grant proposal for ML-based prediction of maintenance timing in maritime operations, applying AI concepts to equipment reliability in a critical logistics domain. +\item Built containerised REST microservices with Express.js, JavaScript, \textbf{Docker} and SQLite for MISSION, a Fraunhofer platform that enabled structured data exchange between maritime logistics stakeholders. +\item Independently set up the team's first Jenkins \textbf{CI/CD} pipeline with quality gates, establishing build automation while developing SCEDAS decision-support software in C\#, .NET, MS SQL and Entity Framework. +\end{rSubsection} + +\begin{rSubsection}{Distributed Backend Engineering \& Automated Quality Gates}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway} +\item Engineered distributed real-time video-transcoding backend components in \textbf{Python} and C++ for Vizrt's broadcast platform, contributing to a production A/V processing pipeline for media customers. +\item Built Python integration and unit tests for A/V streaming, then integrated quality gates into the \textbf{CI/CD} pipeline to improve developer feedback cycles and release reliability for broadcast software. +\end{rSubsection} + +\begin{rSubsection}{Engineering Quality, Application Development \& Integration}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany} +\item Introduced BDD test automation at Generali using Serenity-BDD, Selenium and JBehave; ran the proof of concept, owned implementation, administered Jenkins jobs and trained colleagues in the Java community. +\item Developed Java/J2EE workflow features, migrated WebServices to the XLDeploy deployment process and contributed to an Apache Camel / Spring Boot integration proof of concept for internal applications. +\end{rSubsection} + +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Education} +{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Apr 2012 -- Oct 2013}}\\ +{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\ +{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)} + +{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2009 -- Oct 2012}}\\ +{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection2}{Certifications \& Awards} +\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027). +\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture. +\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML. +\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture. +\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016). +\end{rSection2} + +\begin{center} +\vspace{0.1cm} +\textit{Languages: German (native), English (fluent)} +\end{center} + +\end{document} diff --git a/output/BIS/resume.cls b/output/BIS/resume.cls new file mode 100644 index 0000000..a1670b8 --- /dev/null +++ b/output/BIS/resume.cls @@ -0,0 +1,199 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Medium Length Professional CV - RESUME CLASS FILE +% +% This template has been downloaded from: +% http://www.LaTeXTemplates.com +% +% This class file defines the structure and design of the template. +% +% Original header: +% Copyright (C) 2010 by Trey Hunner +% +% Copying and distribution of this file, with or without modification, +% are permitted in any medium without royalty provided the copyright +% notice and this notice are preserved. This file is offered as-is, +% without any warranty. +% +% Created by Trey Hunner and modified by www.LaTeXTemplates.com +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\ProvidesClass{resume}[2018/09/25 v1.0 Resume class] + +\LoadClass[10pt, a4paper]{article} % Font size and paper type +\usepackage{lastpage} +\usepackage[parfill]{parskip} % Remove paragraph indentation +\usepackage{array} % Required for boldface (\bf and \bfseries) tabular columns +\usepackage{ifthen} % Required for ifthenelse statements +\usepackage{enumitem} + \pagestyle{empty} % Suppress page numbers + +%---------------------------------------------------------------------------------------- +% HEADINGS COMMANDS: Commands for printing name and address +%---------------------------------------------------------------------------------------- + +\def \name#1{\def\@name{#1}} % Defines the \name command to set name +\def \@name {} % Sets \@name to empty by default + +\def \addressSep {$|$} % Set default address separator to a diamond + +% One, two or three address lines can be specified +\let \@addressone \relax +\let \@addresstwo \relax +\let \@addressthree \relax +\let \@addressfour \relax + +% \address command can be used to set the first, second, and third address (last 2 optional) +\def \address #1{ + \@ifundefined{@addresstwo}{ + \def \@addresstwo {#1} + }{ + \@ifundefined{@addressthree}{ + \def \@addressthree {#1} + }{ + \@ifundefined{@addressfour}{ + \def \@addressfour {#1} + } {\def \@addressone {#1} + } + + } + } +} + +% \printaddress is used to style an address line (given as input) +\def \printaddress #1{ + \begingroup + \def \\ {\addressSep\ } + {#1} +% \centerline{#1} + \endgroup + \par + % \addressskip +} + +% \printname is used to print the name as a page header +\def \printname { + \begingroup + % \MakeUppercase + {\namesize\bf \@name} \hfil +% \hfil{\MakeUppercase{\namesize\bf \@name}}\hfil + \nameskip\break + \endgroup +} + +%---------------------------------------------------------------------------------------- +% PRINT THE HEADING LINES +%---------------------------------------------------------------------------------------- + +\let\ori@document=\document +\renewcommand{\document}{ + \ori@document % Begin document + % \begin{center} + \printname % Print the name specified with \name + \@ifundefined{@addressone}{}{ % Print the first address if specified + \printaddress{\@addressone}} + \@ifundefined{@addresstwo}{}{ % Print the second address if specified + \printaddress{\@addresstwo}} + \@ifundefined{@addressthree}{}{ % Print the third address if specified + \printaddress{\@addressthree}} + \@ifundefined{@addressfour}{}{ % Print the third address if specified + \printaddress{\@addressfour}} + + % \end{center} +} + +%---------------------------------------------------------------------------------------- +% SECTION FORMATTING +%---------------------------------------------------------------------------------------- + +% Defines the rSection environment for the large sections within the CV +\newenvironment{rSection}[1]{ % 1 input argument - section name + \sectionskip + {\bf #1} +% \MakeUppercase{\bf #1} % Section title + \sectionlineskip + \hrule % Horizontal line + \begin{list}{}{ % List for each individual item in the section + \setlength{\leftmargin}{0.50em} % Margin within the section + } + \item[] +}{ + \end{list} +} + +\newenvironment{rSection2}[1]{ % 1 input argument - section name + \sectionskip + {\bf #1} % Section title + \sectionlineskip + \hrule % Horizontal line + \medskip + \begin{list}{$\bullet$}{\setlength{\leftmargin}{1.5em}} + \itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics +}{ + \end{list} + \vspace{0.5em} +} + +\newenvironment{rSection3}[1]{ % 1 input argument - section name + \sectionskip + {\bf #1} % Section title + \sectionlineskip + \hrule % Horizontal line + \medskip + \begin{enumerate}[]{\setlength{\leftmargin}{1.5em}} + \itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics +}{ + \end{enumerate} + \vspace{0.5em} +} +%---------------------------------------------------------------------------------------- +% WORK EXPERIENCE FORMATTING +%---------------------------------------------------------------------------------------- + +\newenvironment{rSubsection}[4]{ % 4 input arguments - company name, year(s) employed, job title and location + {\bf #1} \hfill {#2} % Bold company name and date on the right + \ifthenelse{\equal{#3}{}}{}{ % If the third argument is not specified, don't print the job title and location line + \\ + {\em #3} \quad {\em #4} % Italic job title and location + }\smallskip + \begin{list}{$\cdot$}{\leftmargin=1.5em} % \cdot used for bullets, no indentation + \itemsep -0.2em \vspace{-0.2em} % Compress items in list together for aesthetics + }{ + \end{list} + \vspace{0.2 em} % Some space after the list of bullet points +} + + + +%---------------------------------------------------------------------------------------- +% FORMAT C SKILLS COMMANDS +%---------------------------------------------------------------------------------------- + +% Skills group environment: \begin{skillgroup}{Group Name} ... \end{skillgroup} +% Renders bold header + indented dash sub-items. Each \skilldash = exactly 1 rendered line. +\newenvironment{skillgroup}[1]{% + \textbf{#1}\par\nopagebreak% + \vspace{-\parskip}% + \begin{list}{--}{\leftmargin=0.8em \labelsep=0.3em \itemsep=0pt \topsep=0.1em \parsep=0pt \partopsep=0pt}% +}{% + \end{list}% + \vspace{-\parskip}\vspace{0.45em}% +} + +% Single dash sub-item within a skillgroup. Content must fit 1 rendered line. +% Char limit: 119 - (0.5 x bold_char_count) at 10pt +\newcommand{\skilldash}[1]{\item #1} + +%---------------------------------------------------------------------------------------- +% EXPERIENCE SUB-THEME COMMAND +%---------------------------------------------------------------------------------------- + +% Sub-theme underline header within rSubsection +\newcommand{\subtheme}[1]{\item[] \underline{#1}} + +% The below commands define the whitespace after certain things in the document - they can be \smallskip, \medskip or \bigskip +\def\namesize{\huge} % Size of the name at the top of the document +\def\addressskip{\smallskip} % The space between the two address (or phone/email) lines +\def\sectionlineskip{\medskip} % The space above the horizontal line for each section +\def\nameskip{\medskip} % The space after your name at the top +\def\sectionskip{\medskip} % The space after the heading section diff --git a/output/BIS/session_bis_senior_data_analytics_ai.md b/output/BIS/session_bis_senior_data_analytics_ai.md new file mode 100644 index 0000000..1a6b475 --- /dev/null +++ b/output/BIS/session_bis_senior_data_analytics_ai.md @@ -0,0 +1,155 @@ +# Session: Bank for International Settlements — Senior Data & Analytics Engineer – Artificial Intelligence + +## JD Info + +- **File:** JDs/bis_senior_data_analytics_ai.txt +- **JD source:** live scrape 2026-07-10 via BIS careers site +- **Role:** Senior Data & Analytics Engineer – Artificial Intelligence +- **Company:** Bank for International Settlements (BIS), Architecture, Platforms and Intelligence / Banking Technology +- **Bundle:** ML / AI Engineer (primary) + Staff / Senior Data Engineer (hybrid) +- **Format:** Resume (2-page) + 1-page cover letter +- **Location / contract:** Basel, Switzerland; hybrid; 3-year fixed term; deadline 24 Jul 2026 +- **Salary / benefits:** BIS states that salaries and benefits are internationally competitive, normally tax-exempt in Basel, with a defined-benefit pension. Exact offer is not published. + +## JD Analysis + +### Requirements + +| # | Requirement | Match | Evidence | +|---|-------------|-------|----------| +| 1 | Relevant degree or equivalent hands-on experience | Direct | M.Eng. in Software Design & Engineering; 10+ years professional engineering experience | +| 2 | Proven record designing, building and optimising cutting-edge AI solutions | Bridge — MED | Designed and deployed ML inference for 24/7 Bosch semiconductor production; current AI-enabling data foundation at Swisscom. No claim of LLM solution ownership. | +| 3 | Data pipelines, analytical models and data engineering for AI use cases | Direct | Swisscom Component Owner for Kafka/Python ETL and AWS data-platform migration; Bosch production ML data services. | +| 4 | Python | Direct | Expert-level pipeline and application work at Swisscom; data services and ML/NLP contributions at Bosch and Fraunhofer. | +| 5 | LLM application testing and evaluation methodology | Gap | Verified LiteLLM/custom-GPT exposure does not evidence a formal LLM evaluation framework. Do not claim. | +| 6 | Pre-process large unstructured data for AI | Bridge — MED | Bosch image-classification production workflow and contributing NLP/speech-recognition work at Fraunhofer; no claim of large-scale LLM-corpus preparation. | +| 7 | APIs/interfaces integrating AI models with existing platforms | Bridge — MED | Built Python/Java/C# data services and REST microservices; created/used LiteLLM APIs. No claim of owning a model-integration platform. | +| 8 | Monitor and maintain deployed models | Bridge — MED | Operated production ML inference at Bosch plus observability PoC; no evidence of retraining/model-governance ownership. | +| 9 | Financial/banking exposure | Gap — nice to have | No professional treasury, asset-management or banking experience. | +| 10 | Mentor junior colleagues | Gap — nice to have | No verified mentoring evidence to claim. | +| 11 | English | Direct | Fluent; BIS corporate language is English. | + +### ATS Keywords + +- **AI / ML:** applied AI, LLM applications, LLM evaluation, model integration, model monitoring, machine learning, NLP, speech recognition, MLOps +- **Data engineering:** Python, data pipelines, analytical models, data exploration, preprocessing, unstructured data, APIs, production support +- **Platform / operations:** Kubernetes, Docker, AWS, Kafka, Airflow, observability, CI/CD, reliability +- **Domain:** Banking Technology, financial services, treasury, asset management, financial structure +- **Collaboration:** requirements analysis, stakeholders, technical communication, junior support, continuous learning + +### Gap Assessment + +- **Direct:** Python; production data pipelines; cloud data infrastructure; deployed ML inference; Kubernetes/Docker; CI/CD; production support; stakeholder-facing application ownership. +- **Bridge:** Applied-AI engineering through production image-classification inference (HIGH); NLP/speech-recognition contribution at Fraunhofer (MED); API/model-interface work via data services, microservices and LiteLLM (MED); model operations via Bosch production deployment and observability (MED); unstructured-data relevance through image and speech workflows (MED). +- **Gap:** Formal LLM evaluation methodology; direct LLM application ownership; financial/banking domain; verified mentoring. These stay explicit gaps and will not be disguised as experience. + +## Company Context + +- **Mission:** BIS supports central banks and financial stability through international cooperation, banking services, research and technology innovation. +- **This role:** Banking Technology's Architecture, Platforms and Intelligence team is expanding data, analytics and applied-AI applications. BIS has recently launched Project Voyager to build and integrate a portfolio of generative-AI technologies into its activities. +- **Relevant work:** BIS's Project Gaia used LLMs, semantic search and systematic prompting to extract and harmonise climate-related disclosures; its AI work emphasises data quality, privacy, security and resilience in financial services. +- **Culture:** International, English-first, public-purpose organisation with an explicit innovation mandate and hybrid work model. The role is Basel-based; hybrid does not mean fully remote. +- **Why them angle:** Pair production-grade AI/data engineering with the institution's responsible, data-governed use of AI in central banking. Emphasise operating dependable data and ML systems, not claiming to be an LLM researcher. + +## Framing Strategy + +- **Lead narrative:** Infrastructure-first applied-AI engineer: deployed ML inference in a 24/7 manufacturing environment, then built and owned governed cloud data products and pipelines at Swisscom. Brings the operational discipline needed to integrate, run and monitor AI applications in a risk-sensitive institution. +- **Reframing map:** Bosch production ML inference → deployed applied-AI application; Swisscom AWS data mesh / metadata → governed, discoverable data foundation for AI; Fraunhofer ARTUS → contributing NLP/speech-recognition experience; data services / MISSION microservices → APIs and interfaces for integrating AI-enabled services. +- **Emphasize:** Bosch ML inference deployment; Swisscom Data Mesh, metadata and data products; Python/Kubernetes/AWS/Kafka; application/component ownership; stakeholder requirements and production support; responsible data governance. +- **Downplay:** Generic dashboarding; legacy test automation; unverified LLM-evaluation claims; finance-domain claims. +- **CL hooks:** Project Voyager's GenAI portfolio; Project Gaia's LLM-supported analysis of unstructured climate disclosures; BIS emphasis on data quality, privacy, security and resilience for AI in financial services. +- **User directives:** Target BIS only; preserve accuracy over keyword match; do not imply fully remote work or guarantee a compensation upgrade. + +## Critique Context (captured in Phase 0, used in /critique) + +- **Reviewer persona:** Applied-AI/data-platform engineering lead in Banking Technology. They need a pragmatic builder who can make AI applications reliable, safe and usable in an international, risk-sensitive environment. +- **Competitive landscape:** Obvious candidates will have direct LLM/RAG evaluation, financial-services data, and model-lifecycle experience. Dennis differentiates through genuine production ML deployment, enterprise data-platform ownership and operational accountability, but must not try to out-claim them on LLM depth. +- **Domain vocabulary:** applied AI, LLM evaluation, data governance, unstructured data, model integration, production support, observability, financial stability, responsible innovation, central banking. + +## Cover Letter Plan + +- **Institution type:** International financial institution / applied-AI engineering team +- **Paragraph count:** 3 paragraphs; 270–300 words +- **P1 hook:** Production ML inference in Bosch's 24/7 semiconductor fab, paired with BIS's need to operate dependable applied-AI applications. +- **P2–P3 evidence:** Bosch ML deployment and monitoring; Swisscom's governed data products, metadata and AWS data platform; Fraunhofer NLP contribution as supporting—not primary—LLM bridge. +- **Domain pivot:** "While my production work has been in telecom and semiconductor manufacturing rather than banking, it has required the same discipline around reliable data, controlled deployment, observability and stakeholder-facing operation that responsible AI applications demand." +- **Jargon level:** Technical but HR-safe +- **Why them hook:** BIS's Project Voyager and Project Gaia show an institution applying LLMs where data quality, governance and resilience matter. + +## Bullet Plan + +### Swisscom — Staff Data, Analytics & AI Engineer (7 bullets, 14 rendered lines) + +| # | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---:|---| +| * | SW-7 | Governed data products and metadata within Swisscom's Data Mesh as the discoverable foundation for downstream AI workflows | 2L | 2 | Direct / Bridge | +| * | SW-3 | Python applications on Kubernetes with GitLab CI/CD; full containerised delivery and operation | 2L | 2 | Direct | +| * | SW-1 | Scoped migration of legacy Teradata/Oracle ETL to AWS serverless data platform | 2L | 2 | Direct | +| * | SW-2 | Component ownership of Fulfillment Python/Kafka ETL, governance, incident response and on-call SLA | 2L | 2 | Direct | +| o | SW-4 | Stakeholder-facing data products, backlog partnership, automation and production root-cause analysis | 2L | 2 | Direct | +| o | SW-6 | PySpark distributed processing in the Data Lake for large-scale Python/SQL data workloads | 2L | 2 | Bridge | +| + | SW-5 | 2025/26 Security Champion role: designated security point of contact for secure data-pipeline delivery | 2L | 2 | Direct / page-fill addition | + +### Bosch Semiconductor — (Senior) Data & ML Engineer (5 bullets, 10 rendered lines) + +| # | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---:|---| +| * | BS-1 | Production ML inference: Docker/Kubernetes/Ansible image classification in a 24/7 semiconductor fab | 2L | 2 | Direct / strongest AI evidence | +| * | BS-2 | Python/Java/C# data services over Oracle and Hadoop/Impala supplying manufacturing analysis applications | 2L | 2 | Direct / API bridge | +| * | BS-3 | Application Owner for analytics applications and pipelines: SLOs, user enablement, vendor and stakeholder operations | 2L | 2 | Direct | +| + | BS-5 | Co-owned Spotfire analytics platform, built C# extensions and co-presented at TIBCO Analytics Forum 2022 | 2L | 2 | Direct / page-fill addition | +| o | BS-4 | ELK/Kafka anomaly-detection PoC plus Grafana/Prometheus/Loki monitoring for production systems | 2L | 2 | Bridge | + +### Fraunhofer CML — Research Software Engineer (4 bullets, 8 rendered lines) + +| # | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---:|---| +| * | FC-2 | Contributed ML/NLP and speech-recognition components to ARTUS, a safety-critical sea-rescue transcription project | 2L | 2 | Bridge | +| + | FC-4 | Contributed to a research-grant proposal for ML-based predictive maintenance in maritime operations | 2L | 2 | Bridge / page-fill addition | +| o | FC-3 | Built containerised REST microservices for maritime data exchange, showing service/API integration | 2L | 2 | Bridge | +| o | FC-1 | Independently introduced Jenkins CI/CD quality gates while developing decision-support software | 2L | 2 | Bridge | + +### Vizrt — DevOps Engineer (2 bullets, 4 rendered lines) + +| # | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---:|---| +| o | VZ-1 | Python/C++ distributed real-time backend components for a production broadcast platform | 2L | 2 | Bridge | +| o | VZ-2 | Python integration/unit tests and CI/CD quality gates, supporting a testing-discipline bridge without claiming LLM evaluation | 2L | 2 | Bridge | + +### Generali — IT Consultant (2 bullets, 4 rendered lines) + +| # | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---:|---| +| o | GN-1 | Introduced BDD automation, owned the technical implementation and trained colleagues across the Java community | 2L | 2 | Bridge | +| o | GN-3 | Java/J2EE workflow features, WebService deployment migration and Spring Boot/Camel integration PoC | 2L | 2 | Bridge | + +**Confirmed set:** 20 variable bullets / 40 rendered lines. The three marked additions were added at the 2-page fill gate: security responsibility, analytics-platform ownership and an accurately hedged ML research contribution. + +**Forced exclusions:** No direct LLM-evaluation, RAG, LangChain/LangGraph/LlamaIndex, finance/banking, mentoring, or model-retraining claim. Do not use the outdated three-year Swisscom Security Champion wording. Do not imply solo ownership of Swisscom's company-wide Data Mesh. + +**Focus impact:** Bosch production ML, Fraunhofer NLP, Swisscom data products/metadata and operations are elevated over generic dashboards, legacy test automation and RPA. + +**Verified BIS hooks:** Project Gaia uses AI/LLMs and semantic search to extract climate-risk indicators from unstructured corporate disclosures; Project Voyager is BIS's two-year programme for integrating a GenAI technology portfolio into BIS activities. + +## Output Files + +- Resume: `output/BIS/e2e_bis_senior_data_analytics_ai_resume.tex` +- Cover Letter: `output/BIS/e2e_bis_senior_data_analytics_ai_cover_letter.tex` +- Critique: `output/BIS/critique_bis_senior_data_analytics_ai.md` + +## Critique Summary + +- **Score:** 80.5/100 (strong package; structural ceiling is direct LLM evaluation and financial-services experience, not a writing defect). +- **Tier 1:** Improve the page-two Bosch continuation/fill if the two-line-only preference can be relaxed; clarify the documented LiteLLM scope as created/used APIs. +- **Strengths:** Bosch 24/7 production ML, Swisscom governed data and pipeline ownership, clear Python and production-support evidence. +- **Do not add:** LLM evaluation, direct LLM application ownership, banking, model retraining, or mentoring claims without new evidence. + +## Status + +- Phase 0: DONE +- Phase 1: DONE (20 bullets confirmed; 3 page-fill additions documented) +- Phase 2 Resume: DONE +- Cover Letter: DONE (281 words, 1 page; compiled and visually verified) +- Critique: DONE (80.5/100; user finalized without further edits) +- Package: SUBMITTED (2026-07-10) +- **Next:** Await BIS response; retain the critique and portal answers for interview preparation. diff --git a/output/NATO_AI_ENGINEER/NATO_AI_ENGINEER.txt b/output/NATO_AI_ENGINEER/NATO_AI_ENGINEER.txt new file mode 100644 index 0000000..4ed195e --- /dev/null +++ b/output/NATO_AI_ENGINEER/NATO_AI_ENGINEER.txt @@ -0,0 +1,142 @@ +Job Title: Staff Officer (2030 Digitalisation - Artificial Intelligence Engineer) +This vacancy notice is for a NATO-2030 agenda project-linked NATO International Civilian (PLN) post. +This post is limited to a three-year definite duration project. It will be filled as soon as possible. In view +of the urgency of this project, qualified candidates who hold or have recently held a valid NATO or +National security clearance will be given priority consideration. +Please note that the JWC is currently trialling a new organizational structure. Consequently, reporting +lines, job titles, functional alignments and some duties may differ slightly from those outlined in the +vacancy notice. +NATO Body: Joint Warfare Centre (JWC) +Primary Location: Stavanger, Norway +Schedule: Full-Time +Salary (Pay Basis): 93,933 NOK Monthly +Grade: G15 +Clearance Level: NATO Secret (NS) +Application Deadline: 9 August 2026 +The Joint Warfare Centre (JWC) is seeking a Staff Officer (2030 Digitalisation - Artificial Intelligence +Engineer) for its civilian workforce within the CIS Services Branch. +We are building a Data Science Team at NATO’s JWC in spectacular Stavanger, Norway. This team +will lead the transformation of JWC’s exercises to incorporate the latest emerging defence +technologies faster than ever before. The JWC’s AI and Automation initiatives will improve the mission +impact of AI for NATO operations and streamline business processes across the centre. +In this role, you will contribute as a key member of the JWC Data Science Team to deliver AI to NATO +exercises, gaining experience in multi-level security, international, and operational environments. You +will plan, develop, and deliver on a roadmap to bring cutting-edge AI (including latest generation LLMs, +reasoning models, and agentic systems) to operate on NATO’s strategic, data-centric exercises and +programs. Your hands-on experience will carry over as your team delivers and iterates with operators +from across the alliance. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +What the role offers: + High impact, portfolio-level work at the intersection of AI, defence, and NATO operations. + An English-language environment with colleagues from across the 32-nation alliance + An opportunity to live and work in one of Norway’s most beautiful coastal cities, with easy +access to the fjord and flights across Europe. + Tax free salary and privileges. +If you are ready for a challenging role that will leverage your technical skills and expose you +to an international cohort working on challenges at the highest strategic level, we encourage +you to apply. +Principal Duties +The incumbent's duties are: +• Provide subject-matter-expert advice, manage the development, refinement, and delivery of large +language model (LLM) artificial intelligence tools in support of JWC’s digital transformation effort. +• Highlight emerging technologies and opportunities for JWC to enhance its employment of its +digital transformation using machine learning in JWC process improvement. +• Deploy, optimize and manage large language model-powered applications in a JWC production +environment +• Fine-tune existing, pre-trained (LLM) to adapt to future JWC applications. +• Develop multi-modal solutions to address hybrid search +• Develop a roadmap for implementing Data initiatives and integrating NATO's digital +transformation into JWC’s processes and exercise production; +• Develop and apply validation methodologies to verify the accuracy and reliability of large data- +centric programmes, such as Geographic Intelligence and others across the JWC to ensure +cohesive LLM outputs in support of exercise delivery. +• Recommend, coordinate, and initiate projects aimed at sustainably embedding AI-based solutions +into JWC business processes. +• Collaborate with stakeholders, internal teams, and working groups, as needed. +• Provide expert advice and guidance on automation to the JWC’s leadership team and staff. +• Remain abreast of industry trends and best practices in artificial intelligence systems, contributing +to the continual enhancement of analytical processes and tools at JWC NATO. +Essential Qualifications +Education/Training +• University Degree in information technology, economics, statistics, operations research or related +discipline and 3 years function related experience, +• or Higher Secondary education and completed advanced vocational training in that discipline +leading to a professional qualification or professional accreditation with 4 years post related +experience. +Experience +• At least 3 years’ functional experience of developing, managing, and adapting artificial intelligence +systems including Large Language +• Models (LLMs). +• At least 2 year’s functional experience with Python, SQL and Spark or equivalent. +• Certification in Microsoft Azure Data Scientist or equivalent. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +Language +English – Upper Intermediate/Advanced +Desirable Qualifications +Professional Experience +• Problem solving skills: Strong analytical and critical thinking skills to identify patterns, trends and +outliers in data as well as being able to solve complex business problems using data-driven +approaches. +• Knowledge of and experience in programming, machine learning, data management, big data +technology, ethical considerations of data and project management. +Education/Training +• Master’s Degree or equivalent in artificial intelligence systems, computational science, or related +discipline. +• A recognized Project management Qualification (APM, Prince, AgilePM, etc.) +Personal Attributes/Competencies +• Considerable maturity and professional judgement is required to make decisions on to ensure +seamless provision of high-quality support to the JWC. +• High level of organisational and coordination skills. +• Excellent managerial, interpersonal, and communication skills with a visionary view to evolving +requirements to meet future challenges. +• Able to cope with stress and possessing good health. +• Must be able to work as a member of a team in a multi-national environment. +• An analytical, systematic and pro-active approach is important. +• Strong analytical and critical thinking skills to identify patterns, trends and outliers in data as well +as being able to solve complex business problems using data-driven approaches. +• A capacity for original thought, including the incorporation of emerging +• concepts. +• Self-motivated and capable of working under pressure. +Work Environment +The work is normally performed in an office environment. +NOTE: The work both oral and written in this post and in this headquarters as a whole is conducted +mainly in English. +Travel on temporary duty may be required for several conferences. +Irregular working hours may be required, especially during exercises/events +How to Apply for a Project Linked NATO Civilian Post at JWC: +JWC, as an equal opportunities employer, values diverse backgrounds and perspectives and is +committed to recruiting and retaining a diverse and talented workforce. We welcome applications from +nationals of all NATO Member States and strongly encourage women to apply. +Applications are to be submitted, in English, using the NATO Talent Acquisition Platform (NTAP) +(https://nato.taleo.net/careersection/2/jobsearch.ftl?lang-en). Applications submitted by other means +will not be accepted. +NATO UNCLASSIFIED +NATO UNCLASSIFIED +NTAP allows for the adding of attachments. Candidates are to attach a copy of the +qualification(s)/certificate(s) covering the highest level of education and vocational qualifications held +to support their application. +Applications are automatically acknowledged within one working day after submission. In the absence +of an acknowledgement please make sure the submission process is completed or re-submit the +application. Applications will not be accepted after the deadline. +Remarks: + +Notes for candidates: The candidature of NATO redundant staff at grade G15/A-2 will be considered +before any other candidates. +Notes for NATO Civilian Human Resources Managers: if you have qualified redundant staff at +grade G15/A-2, who wish to be considered for this post, please advise JWC Civilian HR no later than +the closing date. +Final interviews are scheduled to take place during the second half of October 2026. +Contract: +This project post is limited to a definite duration of 3 years. There is no guarantee that this post will +continue beyond that period. Successful applicants will be offered a 3-year definite duration +employment contract. Serving staff will be offered a contract in accordance with the NATO Civilian +Personnel Regulations. +Salary: +Starting basic salary is NOK 93,933.00 per month (tax-free). Additional allowances may apply +depending on the personal circumstances of the successful candidate. For further details see NATO +Terms & Conditions. + +For any queries, please contact the Joint Warfare Centre Recruitment Team at +jwc.recruitment@nato.int \ No newline at end of file diff --git a/output/NATO_AI_ENGINEER/critique_nato_ai_engineer.md b/output/NATO_AI_ENGINEER/critique_nato_ai_engineer.md new file mode 100644 index 0000000..da34874 --- /dev/null +++ b/output/NATO_AI_ENGINEER/critique_nato_ai_engineer.md @@ -0,0 +1,229 @@ +# Critique: NATO JWC — Staff Officer (2030 Digitalisation - Artificial Intelligence Engineer) + +**Resume:** `output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex` +**Cover letter:** `output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex` +**JD:** `JDs/NATO_AI_ENGINEER.txt` (file provided; verbatim) +**Date:** 10 July 2026 +**Overall package score:** **77.5/100** + +--- + +## 1. Domain-Specialist Lens + +### Reviewer Persona and Company Context + +The primary reader is likely a JWC CIS Services Branch or Data Science Team lead, supported by civilian HR. At G15, they need someone who can advise senior staff and turn AI prototypes into dependable capabilities for NATO exercises. Generic cloud-tool lists will not impress them; production constraints, security awareness, operational judgement and credible LLM depth will. JWC's AI in Audacious Training work is already moving into implementation and focuses on practical exercise support with operator review. + +### JD Vocabulary Extraction + +| JD term | Importance | Resume match | +|---|---|---| +| LLM tools / applications | Essential | Partial: agent configuration, not delivery ownership | +| Production environment | Essential | Strong bridge: Bosch 24/7 ML | +| Python, SQL, Spark | Essential | Direct | +| Fine-tuning | Essential | Absent | +| Hybrid search / multimodal | Essential | Absent | +| Validation methodologies | Essential | Partial: quality gates and observability | +| Data roadmap / initiatives | High | Partial | +| Multi-level security | High | Partial; no clearance | +| Azure Data Scientist certification | Essential credential | Partial: AWS SAA is adjacent | +| Multinational operational environment | High | Strong bridge: Bundeswehr and Norway | + +### Domain Vocabulary Map + +| Resume wording | JWC interpretation | Assessment | +|---|---|---| +| Production ML | AI delivered under operational constraints | Strong, especially Bosch | +| Domain-grounded LLM agents | Internal LLM application configuration | Accurate; do not upgrade to “agentic system” | +| Data Mesh / metadata | Governed foundation for data-centric operations | Good bridge, not hybrid search | +| CI/CD quality gates | Validation and controlled delivery discipline | Useful bridge, not formal LLM evaluation | +| Officer service | Operational judgement and structured responsibility | Appropriate, distinctive | + +### Gap Ranking + +- **Fatal / likely gate:** three years of functional LLM-system experience; Azure Data Scientist certification if applied literally; current/recent clearance if cleared applicants are available. +- **Serious:** LLM fine-tuning, multimodal/hybrid search, reasoning/agentic-system delivery, formal LLM evaluation and production LLM operations. +- **Cosmetic:** Geographic Intelligence and formal project-management certification. + +### Methodology Transfer Test + +| Achievement | JWC transfer | +|---|---| +| Swisscom LLM-agent configuration | Credible starting point for a domain-grounded exercise assistant, but does not prove secure deployment, evaluation or adaptation. | +| Bosch 24/7 ML inference | Strong evidence of operating AI under high availability and integration constraints. | +| Swisscom governed data products | Relevant to reliable, discoverable data for exercise systems and future AI use cases. | +| Fraunhofer ARTUS NLP | Applied language-AI context in a safety-relevant setting, correctly hedged. | +| Bundeswehr officer service | Supports maturity and organisational familiarity; does not imply NATO service or clearance. | + +### Competitive Landscape + +The obvious fit has current clearance, direct defence/NATO delivery, Azure credentials and recent LLM/RAG, evaluation, fine-tuning or agentic production experience. Dennis offers rare production ML in a continuous industrial environment, staff-level data ownership and authentic German officer service. The hard disadvantage is that the LLM work is internal-agent configuration, not three years of systems engineering. + +--- + +## 2. Five-Perspective Read-Through + +### ATS Robot + +| Keyword group | Status | +|---|---| +| LLM / AI agents | Direct | +| LLM production applications | Partial | +| Python, SQL, Spark | Direct | +| ML deployment, Docker, Kubernetes | Direct | +| Data management, automation | Direct | +| Validation, security | Partial | +| Fine-tuning, hybrid search, multimodal, reasoning | Absent | +| Azure Data Scientist, clearance | Absent | +| Stakeholder and multinational work | Direct / bridge | + +**Match rate:** 13/20 direct or semantic = **65%, marginal**. The missing terms are mostly unearned capabilities and should not be inserted as keywords. + +### Recruiter Glance (10 seconds) + +**Verdict: Forward, with caution.** The target title, Swisscom staff role, M.Eng., German nationality and officer service make the profile unusually legible for a NATO civilian role. Azure and senior LLM depth remain visible risks. + +### HR Screen (30 seconds) + +**Verdict: Borderline phone screen.** Degree, English, nationality, Python/SQL/Spark and relevant experience are clear. The decision is whether AWS SAA satisfies “or equivalent” and whether LLM-agent configuration meets the functional-experience requirement. + +### Hiring Manager Read (2 minutes) + +**Verdict: Maybe interview.** Bosch is the strongest proof point: AI delivered into a 24/7 environment. The manager will value the officer-service context but will immediately probe the Swisscom-agent architecture, evaluation, access controls and adoption. + +**Predicted first question:** “Walk me through the Swisscom agents: what knowledge was supplied, how did you judge answer quality, who used them, and what constraints did you have?” + +### Technical Reviewer (10 minutes) + +**Truthfulness: strong.** LLM wording is restrained; Fraunhofer uses a contributing verb; Bosch ownership is supported; no clearance, Azure or fine-tuning claim is implied. The candidate must be able to distinguish configuring an internal agent from engineering an LLM system. + +**Consistency: clean.** Resume and letter reinforce the same honest boundary. No publication claim is made, correctly. + +--- + +## 3. Eight-Dimension Scoring + +| Dimension | Score | Weight | Weighted | Notes | +|---|---:|---:|---:|---| +| ATS Keyword Match | 6.8/10 | 15% | 10.2 | Good data/ML coverage; core LLM gaps remain. | +| Summary | 8.2/10 | 10% | 8.2 | Clear and honest bridge. | +| Skills Section | 7.5/10 | 10% | 7.5 | Relevant and pruned; lacks requested LLM capabilities for valid reasons. | +| Bullet Quality | 8.0/10 | 25% | 20.0 | Bosch and Swisscom are well selected and credible. | +| Publication Selection | 7.5/10 | 10% | 7.5 | No publications is appropriate; certifications partly compensate. | +| Narrative Coherence | 8.4/10 | 15% | 12.6 | ML delivery → data reliability → NLP → operational context is coherent. | +| Page Fill & Visual | 6.5/10 | 5% | 3.3 | Clean two-page render; page 2 is intentionally sparse. | +| Credibility Signals | 8.2/10 | 10% | 8.2 | 24/7 ML, AWS certification, officer service and international work are strong. | +| **Total** | | **100%** | **77.5/100** | Polished package, capped by hard JD gaps. | + +--- + +## 4. Interview Likelihood + +| Reader | Probability | Key factor | +|---|---:|---| +| ATS | 55% | Exact LLM, Azure, fine-tuning and hybrid-search terms are absent for accurate reasons. | +| Recruiter | 70% | German nationality and officer background make the role change credible. | +| HR | 45% | Azure and three-year LLM requirements may be hard gates. | +| Hiring Manager | 40% | Production ML and defence context earn curiosity; LLM depth remains uncertain. | +| Technical panel | 35% | Agent architecture, evaluation and security answers decide. | + +**Ceiling:** Current **77.5** → with truthful improvements **79–80** → theoretical maximum with current history **82**. The ceiling is direct LLM duration, fine-tuning/hybrid-search evidence and clearance, not wording. + +--- + +## 5. Tiered Improvements + +### Tier 1 — High impact + +1. **Earn Azure Data Scientist Associate before interview only if genuinely achievable.** Do not list it until earned. This is the cleanest route to address the stated credential. **+1.5 to +2.0 points.** +2. **Document the Swisscom-agent work for the application and interview.** Collect only verifiable facts: agent count, users/adoption, knowledge-source handling, quality review, model-selection rationale and data-access controls. Add to the package only if supported. **+1.0 to +1.5 points if facts exist.** +3. **Address clearance and LLM gaps precisely in the application form.** State only truthful clearance history and retain the same distinction between configuration and production LLM ownership. **Risk reduction, not a score increase.** + +### Tier 2 — Medium impact + +1. Swap `Prompt engineering` for `DevSecOps / Security by Design` only if the 2025/26 Security Champion training can be stated precisely. This supports the security environment without implying clearance. **+0.5 points.** +2. Add a clearly labelled current-learning line on LLM evaluation only if it is specific enough to defend. Do not call it experience. **+0.4 points.** +3. Rebalance page 2 only if density matters more than the user’s concise-content directive. **+0.3 points.** + +### Tier 3 — Cosmetic + +1. Align “former German Armed Forces officer” in the summary with the exact dated experience entry. +2. The letter could move the Bosch proof point earlier, but the current Swisscom-first sequence is defensible for an LLM-focused vacancy. + +**Verdict:** Apply Tier 1 only where new credentials or facts genuinely exist. Do not keyword-stuff fine-tuning, hybrid search, Azure or clearance. + +--- + +## 6. Interview Bridge Points + +| Resume topic | Target equivalent | Interview opening line | +|---|---|---| +| Swisscom LLM agents | Domain-grounded exercise assistant | “At Swisscom I configured agents around domain knowledge; for JWC I would begin with user needs, allowed data and how output quality is reviewed.” | +| Bosch ML inference | Production AI for operational exercises | “The difficult part was integrating and operating the model reliably in a 24/7 environment, not only selecting a model.” | +| Swisscom governed data | Data-centric exercise foundation | “My current work makes data products discoverable, governed and reliable, which is the precondition for trustworthy downstream AI.” | +| Fraunhofer ARTUS | Language AI in a safety-relevant setting | “ARTUS gave me applied NLP context; my role was a contribution within a research team.” | +| Quality gates and observability | LLM validation discipline | “I have not run a formal LLM evaluation programme, but I have built quality gates and monitoring around production systems.” | +| Bundeswehr officer service | Operational judgement | “My officer service does not make me a NATO practitioner, but it gave me respect for structured responsibility and operational users remaining in control.” | + +--- + +## 7. Cover Letter Critique + +### 7A. Anti-patterns + +Pass: specific JWC opening, no generic opener, no defensive gap apology, active closing, no banned AI-writing phrases or em-dash excess. LLM scope is configuration, not training or deployment. + +### 7B. Tailoring + +Pass: names JWC and AI in Audacious Training, references operator review, data-centric exercise systems and digital transformation. It could name NATO-Maven Smart System once, but the present hook is already strong and restrained. + +### 7C. Context-Specific Assessment + +The tone is appropriate for an international defence organisation: mission-aware, technical but HR-safe, and not pretending to be a defence-AI specialist. + +### 7D. CL ATS Check + +8/10 high-priority terms appear directly or semantically: AI, LLM, data, Python, Kubernetes, CI/CD, ML, operational/multinational. Azure, fine-tuning, hybrid search and clearance remain correctly absent. + +### 7E. Structural Checks + +- One page; 324 body words, around 336 including closing. This fits the session’s 330–380-word plan. +- Claims trace to resume bullets. The named programme is verified: https://www.act.nato.int/article/ai-audacious-training/ +- Bosch production ML in paragraph 3 is a minor hierarchy issue, not a credibility issue. + +### 7F. Package Cohesion + +The resume stands alone; the letter adds why-JWC context. Both repeat the same honest boundary around LLM work. No contradiction found. + +--- + +## 8. Post-Generation Verification + +### Mechanical + +- [x] Resume compiled to 2 pages; cover letter compiled to 1 page. +- [x] All 14 experience bullets passed the Resume-2L character gate; no OVER violations. +- [x] No header wrap, clipping or broken layout found in visual review. +- [ ] Resume page 2 has more than the target three lines of whitespace. This is an acknowledged user-directed compactness trade-off. + +### Content + +- [x] Configured email appears in both documents. +- [x] Nationality, officer service, roles and dates are internally consistent. +- [x] No unearned clearance, Azure, LangChain/LangGraph, fine-tuning, hybrid-search or production-LLM claim. +- [x] Fraunhofer contribution uses a hedged verb. +- [x] Cover-letter claims trace to resume; JWC hook verified. + +### Structural and Authenticity + +- [x] Company and role are correctly stated. +- [x] Both `.tex` files have standalone preambles and compile. +- [x] Dates and configured email are correct. +- [x] No banned AI-fingerprint words, generic opener or em-dash excess found in the cover letter. +- [x] No resume bullet ends in an “-ing” analysis phrase. + +--- + +## Critique Summary + +**Score: 77.5/100.** The package is polished, accurate and unusually well targeted for a production-ML/data-engineering candidate with authentic defence-context experience. The limitation is structural: the vacancy seeks senior LLM experience, credentials and security context that cannot be recreated through reframing. Submit only with an application-form explanation that remains as precise as the package. diff --git a/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex b/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex new file mode 100644 index 0000000..61d28ba --- /dev/null +++ b/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex @@ -0,0 +1,42 @@ +\documentclass[11pt,a4paper,roman]{moderncv} +\usepackage[english]{babel} +\moderncvstyle{classic} +\moderncvcolor{green} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{ragged2e} +\usepackage[scale=0.80]{geometry} +\usepackage[version=4,arrows=pgf-filled]{mhchem} +\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par} +\microtypesetup{expansion=false} + +\name{Dennis}{Thiessen, M.Eng.} +\address{Bern, Switzerland}{}{} +\phone[mobile]{+41~795~955~585} +\email{dennis@thiessen.io} +\extrainfo{\href{https://linkedin.com/in/dennis-thiessen}{linkedin.com/in/dennis-thiessen}} + +\begin{document} + +\recipient{Hiring Committee}{Joint Warfare Centre\\Stavanger, Norway} +\date{10 July 2026} +\opening{Dear Members of the Hiring Committee,} +\makelettertitle + +\begin{justify} +JWC's AI in Audacious Training work is moving AI from concept into practical support for exercise teams: scenario content is being built, tested, reviewed and refined with operators. I am applying for the Staff Officer (2030 Digitalisation -- Artificial Intelligence Engineer) role because this is the work I want to do: build dependable AI-enabled capabilities that improve operational workflows while keeping the people who use the output involved in delivery and review. That emphasis on expert judgement matches how I approach applied AI. + +At Swisscom, I configure domain-grounded LLM agents in a Swisscom-owned web interface, choosing available models and supplying knowledge bases for Q\&A, migration assistance and data mapping. The role also includes governed data products and metadata on AWS, plus Python services on Kubernetes with GitLab CI/CD. As Component Owner for business-critical Fulfillment pipelines, I work with data quality, governance, incident response and on-call responsibility. That work makes the link between governed inputs and useful AI output concrete. This is the operating discipline I would bring to JWC's data-centric exercise systems. + +Previously at Bosch Semiconductor, I designed and implemented Docker, Kubernetes and Ansible integration for automated image-based defect classification in a 24/7 fab. There was no useful separation between an ML model and its operating environment: data access, delivery, monitoring and user trust all mattered. At Fraunhofer, I also contributed ML and NLP components to ARTUS, a research project for automatic transcription of sea-rescue communications. Together, those experiences give me practical ML delivery and applied language-AI context. + +My six years as a German Armed Forces officer, ending as a Second Lieutenant, provide a genuine connection to the structured, multinational setting of the JWC. As a German national, I meet the nationality condition for this post. I would welcome the opportunity to discuss how my production ML, data engineering and careful LLM-application experience can support JWC's digital transformation and its operators. +\end{justify} + +\vspace{0.3cm} +{Sincerely,\\ +Dennis Thiessen, M.Eng.\\ +Staff Data, Analytics \& AI Engineer\\ +Swisscom (Schweiz) AG} + +\end{document} diff --git a/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex b/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex new file mode 100644 index 0000000..8bf3b80 --- /dev/null +++ b/output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex @@ -0,0 +1,136 @@ +\documentclass{resume} +\usepackage{hyperref} +\usepackage{enumitem} +\usepackage{fontawesome} +\usepackage{tikz} +\usepackage{graphicx} +\hypersetup{ + colorlinks = true, + linkcolor = [rgb]{0.9,0.4,0.4}, + anchorcolor = [rgb]{0.9,0.4,0.4}, + citecolor = [rgb]{0.4,0.4,0.4}, + filecolor = [rgb]{0.4,0.4,0.4}, + urlcolor = [rgb]{0.0,0.0,0.99}, +} +\usepackage{xcolor} +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage{lmodern} +\usepackage[version=4,arrows=pgf-filled]{mhchem} +\usepackage[includefoot,left=0.5in,top=0.5in,right=0.5in,bottom=0.2in,textwidth=7.5in,textheight=10.8in]{geometry} +\usepackage{fancyhdr} +\pagestyle{fancy} +\fancyhf{} +\renewcommand{\headrulewidth}{0pt} +\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}} +\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}} +\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}} + +\name{Dennis Thiessen, M.Eng.} +\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}} +\address{dennis@thiessen.io \\ +41 795 955 585} +\address{Bern, Switzerland $\vert$ Open to relocation to Stavanger, Norway} +\address{{AI Engineer $\vert$ Production ML, LLM Applications \& Data-Centric Operations}} + +\begin{document} + +\vspace{-0.15cm} + +\begin{rSection}{Summary} +Data and ML engineer with 11+ years building data platforms and applied AI in telecom and manufacturing. At Swisscom I configure domain-grounded LLM agents for Q\&A and migration/data-mapping assistance, and build governed AWS data products. At Bosch I designed and deployed containerised ML inference for image classification in a 24/7 semiconductor fab; at Fraunhofer I contributed NLP for sea-rescue transcription. German national and former German Armed Forces officer, I bring engineering discipline to international settings. +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Technical Skills} + +\begin{skillgroup}{LLM Applications \& Applied AI} +\skilldash{\textbf{Python}, LLM-agent configuration, model selection, domain knowledge bases and Q\&A} +\skilldash{Production \textbf{ML} inference, image classification, MLOps, applied NLP and speech recognition} +\skilldash{Prompt engineering, migration and data-mapping assistance, custom GPTs with domain knowledge} +\end{skillgroup} + +\begin{skillgroup}{Data Engineering \& Governance} +\skilldash{\textbf{SQL}, \textbf{PySpark}/Spark, \textbf{Apache Kafka}, \textbf{Apache Airflow}, ETL/ELT design and operation} +\skilldash{Data products, metadata management, data governance, data quality and data modelling} +\skilldash{Oracle, Teradata, Hadoop/Impala, Athena, Redshift and MS SQL} +\end{skillgroup} + +\begin{skillgroup}{Cloud \& Production Delivery} +\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, Step Functions), SAA-certified} +\skilldash{\textbf{Docker}, \textbf{Kubernetes}, Ansible, GitLab CI/CD, Jenkins, CloudFormation and serverless delivery} +\end{skillgroup} + +\begin{skillgroup}{Validation, Monitoring \& Quality} +\skilldash{\textbf{Grafana}, \textbf{Prometheus}, Loki and ELK; alerting, incident response and production support} +\skilldash{CI/CD quality gates, test automation, code review and structured root-cause analysis} +\end{skillgroup} + +\begin{skillgroup}{Certifications} +\skilldash{\textbf{AWS Certified Solutions Architect -- Associate} (active to Sep 2027), Data Engineering with AWS} +\skilldash{iSAQB CPSA -- Foundation, IBM AI Engineering Specialization, AI for Trading Nanodegree} +\end{skillgroup} + +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Professional Experience} + +\begin{rSubsection}{LLM Applications, Governed Data Products \& Production Delivery}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland} +\item Configured domain-grounded LLM agents in a Swisscom-owned web interface, selecting models and supplying knowledge bases for Q\&A, migration assistance and data mapping across enterprise data work. +\item Built governed data products and metadata management within Swisscom's Data Mesh on \textbf{AWS} (Glue, Athena, CloudFormation), creating discoverable sources for dependable analytics and downstream AI use. +\item Designed, deployed and operated \textbf{Python} data applications on \textbf{Kubernetes} with \textbf{GitLab CI/CD}, owning containerised delivery from build and test through deployment and operation in an agile DevOps team. +\item Owned business-critical Fulfillment \textbf{ETL} pipelines from Oracle and \textbf{Kafka} to Teradata in \textbf{Python}; used PySpark for distributed workloads and maintained data quality, governance and on-call SLA. +\item Led migration of my domains' Oracle/Teradata \textbf{ETL} to Swisscom's \textbf{AWS} platform (Glue, Athena/Iceberg, Redshift and \textbf{Airflow}), reducing manual operations through scalable serverless processing for analytics. +\end{rSubsection} + +\begin{rSubsection}{Production ML, Data Services \& Operational Reliability}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany} +\item Designed and implemented ML inference integration for a 24/7 semiconductor fab, using \textbf{Docker}, \textbf{Kubernetes} and Ansible to automate image-based defect classification on active 300mm wafer production lines. +\item Served as Application Owner for semiconductor analytics applications and pipelines, defining SLOs, training users, maintaining documentation and coordinating stakeholders for stable 24/7 operations. +\item Delivered an anomaly-detection proof of concept with ELK and \textbf{Kafka} on \textbf{Docker}, adding \textbf{Grafana}, \textbf{Prometheus} and Loki to test centralised monitoring and alerting for semiconductor manufacturing systems. +\item Built \textbf{Python}, Java and C\# data services over OracleDB and Hadoop/ImpalaSQL, supplying analysis teams with structured process and defect data for quality monitoring in a high-throughput 24/7 fab. +\end{rSubsection} + +\begin{rSubsection}{Applied NLP \& Quality-Controlled Delivery}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany} +\item Contributed \textbf{ML} and NLP components to ARTUS, a Fraunhofer research project for automatic transcription of sea-rescue communications, applying speech recognition in a safety-critical maritime setting. +\item Independently established Jenkins \textbf{CI/CD} quality gates for SCEDAS decision-support software in C\#, .NET and MS SQL, introducing reliable build automation and release checks to the research team. +\end{rSubsection} + +\begin{rSubsection}{Distributed Software Delivery \& CI/CD Quality Gates}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway} +\item Engineered Python/C++ components for distributed video transcoding and built Python A/V tests with CI/CD quality gates, supporting release delivery for Vizrt's Bergen-based broadcast software team. +\end{rSubsection} + +\begin{rSubsection}{Technical Ownership \& Team Training}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany} +\item Introduced BDD test automation at Generali, running the initial proof of concept, owning technical implementation, administering Jenkins jobs and training colleagues within the Java community. +\end{rSubsection} + +\begin{rSubsection}{Officer Service}{\textcolor{black!60}{Jul 2008 -- Nov 2014}}{Officer, German Armed Forces (Bundeswehr)}{Germany} +\item Completed officer candidate training and officer school during six years in the German Armed Forces, leaving service as Second Lieutenant with experience in a structured operational environment. +\end{rSubsection} + +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection}{Education} +{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Apr 2012 -- Oct 2013}}\\ +{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\ +{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)} + +{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2009 -- Oct 2012}}\\ +{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany +\end{rSection} +\vspace{-0.15cm} + +\begin{rSection2}{Certifications \& Awards} +\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027). +\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture. +\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML. +\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture. +\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016). +\end{rSection2} + +\begin{center} +\vspace{0.1cm} +\textit{German national; English fluent} +\end{center} + +\end{document} diff --git a/output/NATO_AI_ENGINEER/session_nato_ai_engineer.md b/output/NATO_AI_ENGINEER/session_nato_ai_engineer.md new file mode 100644 index 0000000..e15c42f --- /dev/null +++ b/output/NATO_AI_ENGINEER/session_nato_ai_engineer.md @@ -0,0 +1,160 @@ +# Session: NATO Joint Warfare Centre Staff Officer (2030 Digitalisation – AI Engineer) + +## JD Info +- **File:** `JDs/NATO_AI_ENGINEER.txt` (copied to this folder) +- **JD source:** file provided by user; verbatim NATO JWC vacancy notice +- **Role:** Staff Officer (2030 Digitalisation – Artificial Intelligence Engineer) +- **Company:** NATO Joint Warfare Centre (JWC), Stavanger, Norway +- **Bundle:** ML / AI Engineer (primary) + Staff / Senior Data Engineer (secondary) +- **Format:** Resume (2-page, `resume.cls`) + 1-page cover letter +- **Salary/Details:** G15; NOK 93,933 monthly tax-free starting salary; 3-year project-linked post; deadline 9 August 2026; NATO Secret clearance required. + +## JD Analysis +### Requirements +| # | Requirement | Match | Evidence | +|---|-------------|-------|----------| +| 1 | Relevant university degree plus 3+ years' experience | Direct | M.Eng. in Software Design & Engineering; 10+ years of professional software/data/AI engineering. | +| 2 | 3+ years developing, managing and adapting AI systems including LLMs | Direct experience / duration gap | Configured domain-grounded LLM agents in a Swisscom-owned web interface, selecting available models for Q&A and migration/data-mapping assistance; production ML inference at Bosch and NLP/speech-recognition research at Fraunhofer. The duration does not yet support a three-year LLM claim. | +| 3 | 2+ years Python, SQL and Spark/equivalent | Direct | Long-running Python and SQL delivery; PySpark confirmed at Swisscom. | +| 4 | Microsoft Azure Data Scientist certification or equivalent | Bridge (recruiter decision) | Active AWS Certified Solutions Architect – Associate, production AWS data-platform work, and IBM AI Engineering learning are credible adjacent evidence; do not state that they are equivalent to the Azure Data Scientist certification. | +| 5 | Production AI deployment, optimisation and management | Direct (ML), gap (LLM-specific) | Designed and implemented ML-model integration strategy for 24/7 Bosch manufacturing; no prior production LLM deployment asserted. | +| 6 | Fine-tuning, multimodal hybrid search, reasoning/agentic systems | Gap | No verified ownership. Learn-now plan may be described only outside experience claims. | +| 7 | Validation, data management, ethical AI and roadmap delivery | Bridge (high) | Quality gates, governed enterprise data products, component/application ownership, and ML deployment; no formal LLM evaluation programme claimed. | +| 8 | Stakeholder advice, coordination and multinational operational environment | Bridge (high) | Staff-level component ownership and cross-team delivery; German Armed Forces officer service (6 years, left as Second Lieutenant); Norwegian role at Vizrt. | +| 9 | English advanced; national of a NATO member state | Direct | English fluent; German national (user-confirmed), therefore eligible to apply as a national of a NATO member state. | +| 10 | Current or recent NATO/National clearance preferred | Gap / verify | Military service is relevant context, but no current or recent clearance is asserted. | + +### ATS Keywords +- **ML/AI:** artificial intelligence, machine learning, LLM, reasoning models, agentic systems, model validation, responsible AI +- **LLM delivery:** LLM-powered applications, production deployment, fine-tuning, multimodal, hybrid search, evaluation, reliability +- **Data/engineering:** Python, SQL, Spark, PySpark, data management, data-centric programmes, automation, Kubernetes, Docker, CI/CD +- **Domain:** NATO, digital transformation, exercises, operational environment, multi-level security, interoperability, Geographic Intelligence +- **Leadership:** roadmap, stakeholder collaboration, subject-matter expertise, coordination, process improvement, multinational environment + +### Gap Assessment +- **Direct:** Configuration of domain-grounded LLM agents for Q&A and task assistance; Python, SQL, PySpark; production ML integration; containerisation/orchestration; data pipelines; M.Eng.; advanced English; German nationality; officer-service and international-work context. +- **Bridge:** Production ML reliability and CI/CD → production LLM operating discipline; NLP/speech-recognition contribution → language-AI context; data quality/quality gates → LLM evaluation methodology; officer and Staff-level ownership → operational coordination and mature judgement. +- **Gap:** Three years of LLM systems; LLM fine-tuning; reasoning systems; multimodal hybrid search; formal LLM evaluation; current/recent clearance. Do not claim any of these. “Agent” refers only to the verified Swisscom assistant work; avoid broader agentic-system ownership. Azure Data Scientist remains unearned; present the active AWS certification and directly relevant AWS work as adjacent evidence, not as an equivalent credential. + +## Company Context +- **Mission:** The JWC prepares NATO through large, complex operational and strategic exercises. Its 2030 transformation work uses data and AI to modernise exercise planning and delivery. +- **This role:** Help establish the JWC Data Science Team and move AI—including LLMs—into production, with validation, security and operational usability central to success. +- **Culture:** English-language, multinational, mission-led and operationally rigorous; JWC’s 2026–2030 campaign stresses digital infrastructure, AI-enabled tools, modelling/simulation, interoperability and rapid adoption. +- **Why them angle:** Dennis can contribute production ML/data engineering discipline and an informed military-operational perspective to a team that must turn AI experiments into dependable exercise capability. Recent JWC work with Maven Smart System and AI-enabled wargaming makes that connection concrete. + +## Framing Strategy +- **Lead narrative:** Production-minded data and ML engineer who has deployed ML in a constrained 24/7 environment, configures domain-grounded LLM agents, builds reliable data foundations, and brings genuine German Armed Forces officer experience to a NATO exercise setting. +- **Reframing map:** Swisscom web-interface LLM-agent configuration with selectable models and domain knowledge → applied LLM delivery; Bosch ML model integration → production AI delivery; Swisscom component ownership/data quality → dependable data-centric operations; Fraunhofer speech recognition → applied NLP foundation; quality gates/observability → validation and reliability discipline; officer service → mature judgement in a structured, multinational defence environment. +- **Emphasize:** Bosch ML deployment; Python/SQL/PySpark; Kubernetes/Docker/CI/CD; Fraunhofer NLP; secure/reliable enterprise delivery; German nationality; Bundeswehr officer service (six years; Second Lieutenant); prior Norway experience. +- **Downplay:** Generic analytics/dashboard detail, older unrelated software work, exhaustive tool lists, and generic leadership language. +- **LLM positioning, stated honestly:** Lead with Swisscom configuration of domain-grounded LLM agents for Q&A and migration/data-mapping assistance, using “grounded” or “configured” rather than “trained.” Models are selected in a Swisscom-owned web interface; do not imply API engineering, model hosting or production deployment. Include verified LiteLLM API/custom-GPT exposure in skills only if it refers to separate, confirmed work. In the cover letter, present current structured learning in evaluation, retrieval/hybrid search, agent safety and fine-tuning as preparation—not prior ownership. Pursue Azure Data Scientist Associate only if realistically completable before application/interview; it cannot be listed until earned. +- **Certification positioning:** Put `AWS Certified Solutions Architect – Associate (active)` prominently in Certifications and pair it with the AWS migration/data-platform bullet. It supports the JD's “or equivalent” wording as cloud architecture and production-data evidence, but the resume and cover letter must never call it Azure Data Scientist equivalent. +- **AI-authenticity guardrails:** Use fewer bullets, each anchored to a specific system, environment and responsibility. Avoid generic summaries, keyword stuffing, uniform verb–metric–method formulas, inflated superlatives and claims that cannot be discussed technically in interview. Text-detection tools are not reliable proof; credibility comes from verifiable, internally consistent detail. +- **CL hooks:** JWC’s AI-enabled exercise transformation; AI in Audacious Training/Maven Smart System; practical production reliability under operational constraints; a German officer who later built AI/data systems in Switzerland, Germany and Norway. +- **User directives:** Compact CV/resume with only decisive evidence; no hallucinated LLM ownership; German nationality is an application-eligibility advantage; Bundeswehr officer service should be used as relevant defence-context evidence. + +## Critique Context (captured in Phase 0, used in /critique) +- **Reviewer persona:** JWC Data Science/CIS leader screening for someone credible in both production AI and NATO operational culture; interested in evidence, security awareness, judgement and delivery—not AI hype. +- **Competitive landscape:** Obvious fits will have current clearance, direct LLM fine-tuning/RAG/agentic production work, Azure certification and perhaps NATO/defence AI experience. Dennis differentiates with production ML in a continuous industrial setting, robust data infrastructure and authentic military context, but must not conceal the LLM gap. +- **Domain vocabulary:** exercise delivery, interoperability, data-centric operations, multi-level security, validation, digital transformation, operational environment, AI-enabled decision support, responsible use. + +## Cover Letter Plan +- **Institution type:** International defence organisation / operational public sector +- **Paragraph count:** 4 paragraphs, 330–380 words +- **P1 hook:** JWC’s shift from AI experiments to AI-enabled exercise planning and delivery, including AI in Audacious Training. +- **P2-P3 evidence:** Bosch 24/7 ML integration; Swisscom production data ownership and Python/Kubernetes delivery; Fraunhofer NLP contribution; concise officer-service and Norway context. +- **Domain pivot:** “While my previous LLM exposure is limited to verified API/custom-GPT work, I am deliberately building the evaluation, retrieval and safety practices required for secure LLM delivery; my production ML and data-platform experience provide the operational foundation.” +- **Jargon level:** Technical but HR-safe; use NATO terminology only where it accurately reflects the JD. +- **Why them hook:** Contribute reliable, operator-aware AI delivery to JWC's multinational exercise transformation. + +### Hook Verification +- **Claim used:** JWC's AI in Audacious Training work is moving into practical support for exercise teams, with AI-enabled scenario content tested, reviewed and refined with operators. +- **Evidence:** NATO ACT reports that the project transferred to JWC implementation in January 2026, is moving from prototype capability toward repeatable exercise value, and evaluates AI-enabled exercise design and execution with practical use and operator feedback. +- **Source:** https://www.act.nato.int/article/ai-audacious-training/ + +## Bullet Plan + +### Swisscom — Staff Data, Analytics & AI Engineer (5 bullets, 10 rendered lines) + +| | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---|---| +| * | SW-7 | Governed data products and metadata management within Swisscom's Data Mesh on AWS; frame as trustworthy, discoverable data foundations for downstream AI—not as agentic or hybrid-search delivery. | Resume-2L | 2 | Bridge | +| * | SW-3 | Operated Python data applications on Kubernetes with GitLab CI/CD; production delivery and reliability. | Resume-2L | 2 | Direct | +| * | SW-2 + SW-6 | Component ownership of business-critical Python/Kafka ETL, with PySpark distributed processing; data availability, quality, governance and on-call responsibility. | Resume-2L | 2 | Direct | +| * | SW-1 | Led scoped migration of legacy ETL to AWS cloud-native services; reliable, scalable data infrastructure for AI/analytics workloads. | Resume-2L | 2 | Bridge | +| * | SW-8 | Configured LLM agents in a Swisscom-owned web interface, selecting available models and supplying a domain knowledge base for Q&A, migration and data-mapping assistance; do not imply fine-tuning, API engineering, hybrid search or production deployment. | Resume-2L | 2 | Direct | +| o | SW-5 | Designated team security point of contact (2025/26); cloud-security and DevSecOps training. Use only if space permits; not an award and not a clearance. | Resume-2L | 2 | Bridge | + +### Bosch Semiconductor — Data & ML Engineer (4 bullets, 8 rendered lines) + +| | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---|---| +| * | BS-1 | Designed and implemented containerised ML inference for automated image-based defect classification in a continuous 24/7 production environment. | Resume-2L | 2 | Direct | +| * | BS-3 | Application ownership for semiconductor analytics applications and upstream pipelines: SLOs, documentation, user training, vendors and stable operation. | Resume-2L | 2 | Direct | +| * | BS-4 | ELK/Kafka anomaly-detection proof of concept with Grafana/Prometheus/Loki observability; validation/reliability bridge, clearly labelled PoC. | Resume-2L | 2 | Bridge | +| * | BS-2 | Python/Java/C# data services over OracleDB and Hadoop/ImpalaSQL for analysis teams; structured data access in a high-throughput environment. | Resume-2L | 2 | Direct | +| x | BS-5 | Spotfire ownership and conference presentation. Useful evidence of communication, but not selective enough for this AI/defence resume. | -- | -- | Weak | + +### Fraunhofer CML — Research Software Engineer (2 bullets, 4 rendered lines) + +| | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---|---| +| * | FC-2 | Contributed ML and NLP/speech-recognition components to ARTUS, an automatic sea-rescue transcription research project; preserve the contributing role. | Resume-2L | 2 | Direct | +| * | FC-1 | Independently introduced Jenkins CI/CD quality gates for a decision-support system; verification and delivery-discipline bridge. | Resume-2L | 2 | Bridge | +| x | FC-3 | Maritime microservices research prototype. Solid engineering, but redundant with newer Kubernetes/Docker experience. | -- | -- | Weak | +| x | FC-4 | Predictive-maintenance grant contribution. No outcome claim; reserve for cover-letter context only. | -- | -- | Weak | + +### Vizrt — DevOps Engineer, Bergen, Norway (1 bullet, 2 rendered lines) + +| | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---|---| +| * | VZ-1 + VZ-2 | Python/C++ distributed backend delivery plus Python test automation and CI/CD quality gates; concise evidence of international Norway experience and operational-quality discipline. | Resume-2L | 2 | Bridge | + +### Generali / Capgemini (0 recommended bullets) + +| | ID | Achievement | Variant | Lines | JD Match | +|---|---|---|---|---|---| +| x | GN-1 | Introduced BDD and led technical test-automation adoption. Strong early-career initiative, but not decisive here. | -- | -- | Weak | +| x | GN-2–4, CA-1 | RPA, enterprise Java and early test automation. Omit to preserve relevance and readable density. | -- | -- | Weak | + +### Additional service line (not an achievement bullet) + +`German national | German Armed Forces Officer, 2008–2014; left service as Second Lieutenant` — place in a compact Additional Information line if the template permits. It establishes NATO eligibility and operational context; it does not imply technical, current-clearance or NATO-service experience. + +**Recommended set:** 14 bullets / 28 rendered lines. Added after visual page-fill review: GN-1 (Generali technical ownership and team training) and BW-1 (German Armed Forces officer service). No other reserve bullets will be added unless the user requests them. **Budget Gate: PASS (user-directed compact exception).** + +**Forced exclusions:** Any LLM fine-tuning, broader agentic-system ownership, hybrid search, multimodal solutions, formal LLM evaluation, Azure Data Scientist certification, current/recent clearance, and the disproven “three consecutive years” Security Champion claim. + +**Focus-directive impact:** The standard ML/AI bundle would include more general data-engineering and older-career material. This plan instead adds direct Swisscom LLM-agent work and the scoped Data Mesh/metadata bridge, keeps officer service visible, and removes older or generic detail. It uses exact JD terms only where backed by work; no bullet claims fine-tuning or production LLM ownership. + +## Output Files +- Resume: `output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_resume.tex` +- Cover Letter: `output/NATO_AI_ENGINEER/e2e_nato_ai_engineer_cover_letter.tex` +- Critique: `output/NATO_AI_ENGINEER/critique_nato_ai_engineer.md` + +## Critique Summary +- **Score:** 77.5/100 +- **Key findings:** Strong production ML/data-platform evidence, disciplined LLM framing, German nationality and officer-service relevance. The ceiling is direct LLM duration, fine-tuning/hybrid-search depth, Azure credential and current clearance. +- **Tier 1 fixes:** Earn Azure Data Scientist only if genuinely achievable; document the Swisscom LLM-agent scope for application/interview; state clearance history truthfully in the application form. + +## Status +- Phase 0: DONE +- Phase 1: DONE (14 bullets confirmed after user revision) +- Phase 2 Resume: IN_PROGRESS + - Summary: DONE + - Skills: DONE + - Experience: DONE (14 bullets; Generali and Bundeswehr added; skills trimmed to NATO-relevant evidence) + - Compile: DONE (2 pages; all 14 experience bullets in Resume-2L range; visual layout verified) +- Resume: DONE +- Cover Letter: DONE (324 body words; 1 page; hook verified; visual and authenticity checks passed) +- Critique: DONE (77.5/100; user approved finalization) +- **Status:** SUBMITTED 2026-07-10 via NTAP. +- **Submission files:** `Dennis_Thiessen_Resume.pdf` and `Dennis_Thiessen_Cover_Letter.pdf` +- **Next CL:** /make-cl output/NATO_AI_ENGINEER/session_nato_ai_engineer.md +- **Next Critique:** /critique output/NATO_AI_ENGINEER/session_nato_ai_engineer.md + +## Resume Point +- The original 12-bullet draft compiled cleanly to two pages but had excessive page-2 whitespace. +- User chose a compact revision: add Generali and Bundeswehr experience only; do not add the other available bullets. Skills were reduced to direct NATO-relevant evidence. +- Final verification: source compiled to two pages; every experience bullet passed the Resume-2L character gate; PDF pages were visually reviewed. Remaining page-2 white space is a deliberate result of the user’s concise-content directive. +- Cover letter verification: 324 body words (about 336 including closing), one-page render, verified JWC AI in Audacious Training hook, and no AI-fingerprint rule violations. diff --git a/resume_builder/experience/experience_bundeswehr.md b/resume_builder/experience/experience_bundeswehr.md new file mode 100644 index 0000000..814e06a --- /dev/null +++ b/resume_builder/experience/experience_bundeswehr.md @@ -0,0 +1,24 @@ +# Experience: Officer — German Armed Forces (Bundeswehr) +## July 2008 – November 2014 | Germany + +### Cross-Position Section + +**Source:** User confirmation (2026-07-10); thiessen_linkedin_profile.md + +**Career arc framing:** Dennis completed the officer candidate course and officer school during six years in the German Armed Forces, leaving service as Second Lieutenant. This is relevant to NATO and defence roles as authentic operational and organisational context. It was not a technical AI or NATO civilian role, and no current or former clearance should be inferred. + +### Achievement BW-1: Officer Training and Service + +**User's role:** Officer candidate / officer +**Status:** Completed service; resigned as Second Lieutenant + +**Context:** Completed officer candidate training and officer school, then served in the German Armed Forces for six years. + +**Safe bullet direction:** Completed officer candidate training and officer school during six years in the German Armed Forces (Bundeswehr), leaving service as Second Lieutenant. Do not imply a current clearance, NATO service, combat role, technical AI work, or responsibilities not verified. + +**ATS keywords:** German Armed Forces, Bundeswehr, officer, multinational environment, operational context, structured leadership + +**Reframing notes:** +- Defence / NATO roles: Include as concise context for operational judgement and organisational familiarity. +- All other roles: Omit or retain only as an additional-information line. +- Accuracy: State only verified training, service duration and rank. diff --git a/resume_builder/experience/experience_swisscom.md b/resume_builder/experience/experience_swisscom.md index fd215f6..5539efc 100644 --- a/resume_builder/experience/experience_swisscom.md +++ b/resume_builder/experience/experience_swisscom.md @@ -166,6 +166,25 @@ --- +### Achievement SW-8: Domain-Grounded LLM Agents for Q&A and Task Assistance + +**Source:** User-confirmed current Swisscom work (2026-07-10) +**User's role:** Creator / configurator of the agents in a Swisscom-owned web interface +**Status:** Models are selectable in the web interface. Deployment, adoption, API and retrieval implementation are not known — do not call production deployment, fine-tuning, RAG, hybrid search, API engineering, or broader agentic-system ownership without further evidence. + +**Context:** Dennis configured LLM agents in a Swisscom-owned web interface, selecting from available models and supplying a domain-specific knowledge base for question answering and domain tasks, including migration assistance and data mapping. This is direct hands-on LLM application configuration, distinct from model training, fine-tuning or API-level deployment. + +**Safe bullet direction:** Configured domain-grounded LLM agents in a Swisscom-owned web interface, selecting available models and supplying a knowledge base for Q&A, migration assistance and data mapping. Add only verified adoption, API or retrieval details. + +**Key skills:** LLM application configuration, model selection, AI agents, domain knowledge bases, question answering, migration assistance, data mapping +**ATS keywords:** LLM-powered applications, AI agents, model selection, knowledge grounding, question answering, workflow automation, data mapping +**Reframing notes:** +- ML/AI: HIGH for LLM-specific roles; lead with applied LLM delivery, then connect reliable data foundations and evaluation discipline. +- Accuracy: Never say “trained an LLM” unless model weights were fine-tuned. Prefer “grounded,” “configured,” or “provided with a domain-specific knowledge base.” +- Production: Use “internal” or “prototype” only if true; otherwise omit deployment-status language until verified. + +--- + ## Position Summary | Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform | @@ -177,3 +196,4 @@ | Security Champion | SW-5 | MED | LOW | MED | HIGH | | PySpark | SW-6 | MED | LOW | MED | MED | | Data Mesh / Data Products / Metadata (agentic foundation) | SW-7 | HIGH | MED | HIGH | HIGH | +| Domain-Grounded LLM Agents | SW-8 | MED | LOW | HIGH | MED |