first commit

resume_builder/bundles/bundle_analytics_engineer.md

@@ -0,0 +1,123 @@
+# Bundle: Analytics Engineer
+
+> Target employers: Data-driven companies, BI/analytics teams
+> Tier: 2 — strong with targeted emphasis
+> Config key: bundle_analytics_engineer.md
+
+---
+
+## S1: Role Profile & Priority Matrix
+
+**Positioning:** Dennis bridges the gap between pure data engineering and analytics — he has built and owned the pipelines *and* delivered the data products and dashboards that stakeholders actually use. His B2B analytics delivery at Swisscom (Switzerland's largest telco), Application Owner experience at Bosch (semiconductor analytics platforms), and domain depth in semiconductor data (defect management, parameter testing, process analysis) make him a credible Analytics Engineer candidate who understands both the technical infrastructure and the analytical outcomes.
+
+**Key differentiator for this role type:** Most Analytics Engineer candidates are either BI-first (lighter on engineering) or DE-first (lighter on business context). Dennis has both: owned pipelines AND delivered B2B data products AND held application ownership for analytics platforms.
+
+### Priority Matrix
+
+| Priority | Achievement IDs | Rationale |
+|----------|----------------|-----------|
+| HIGH | SW-4, SW-1, SW-2, BS-3 | Core analytics signals: B2B products, AWS data infra, pipeline ownership, analytics platform owner |
+| MED | SW-3, SW-6, BS-1 (semi JDs), BS-2, FC-1 | Supporting: K8s delivery, PySpark, ML analytics at Bosch, data service depth |
+| LOW | SW-5, BS-4, FC-2, FC-3, VZ-1, VZ-2, GN-1, GN-2 | Infrastructure/testing — not primary signal for analytics audience |
+
+**2-page resume bullet allocation (typical):**
+- Swisscom: 3–4 bullets (SW-4, SW-2, SW-1, SW-3)
+- Bosch: 2–3 bullets (BS-3, BS-2; +BS-1 for semi JDs)
+- Fraunhofer: 1 bullet (FC-1 compressed or omit)
+- Vizrt: 1 bullet (combined or omit)
+- Generali: 1 bullet (GN-1 or omit)
+
+---
+
+## S2: Summary Guide
+
+**Headline pattern:**
+> "Analytics Engineer | Python · SQL · AWS · Teradata | Data Products, Pipeline Ownership & Stakeholder Analytics"
+
+**Building blocks:**
+- "data products for business stakeholders" or "B2B analytics delivery"
+- "end-to-end pipeline ownership" (show both build and deliver)
+- "analytics platform ownership" (App Owner at Bosch)
+- "AWS cloud-native stack" (Athena, Glue, Redshift — standard analytics stack)
+- Semiconductor domain angle (for semi JDs): "semiconductor manufacturing analytics — defect management, parameter testing, process analysis"
+
+**Tone:** Pragmatic engineer who cares about outcomes, not just pipelines. Business-aware. Bridges technical depth and analytical impact.
+
+**Avoid:**
+- Leading with ML/AI framing (not the primary signal here)
+- Heavy infrastructure language (Kubernetes, Ansible) unless JD asks for it
+- Overplaying test automation background
+
+---
+
+## S3: Achievement Reframing Map
+
+| ID | Default Framing | This Role's Framing | Key Metric / Signal |
+|----|----------------|--------------------|--------------------|
+| SW-4 | B2B products + automation | **Lead bullet** — "Delivered data products, analyses and dashboards for B2B stakeholders; drove automation of recurring technical workflows" | Stakeholder-facing delivery |
+| SW-2 | Component Owner ETL | "Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) — ensuring data availability for downstream analytics with SLA accountability" | Pipeline → analytics link |
+| SW-1 | AWS migration | "Migrated ETL stack to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow) — enabling scalable, query-optimized analytics on a cloud data lakehouse" | Iceberg/Athena = analytics stack |
+| BS-3 | Application Owner | "Application Owner for semiconductor data analysis platforms — defined SLOs, trained users, managed vendor relationships and stakeholder expectations" | Ownership + analytics platform |
+| BS-2 (generic) | Data services | "Built data services supplying analysis teams with on-demand structured access to manufacturing process data" | Analytics enablement framing |
+| BS-2 (semi JD) | Data services | "Built data services enabling Defect Management, Parameter Testing and Process Analysis teams with on-demand data access" | Semiconductor domain specificity |
+| BS-1 (semi JD) | ML inference | "Automated image-based defect classification for semiconductor production — enabling defect management analytics without manual inspection" | Semi domain + analytics outcome |
+| SW-3 | K8s/CI/CD | "Deployed and operated Python data applications on Kubernetes with GitLab CI/CD automation" | Engineering credibility signal |
+
+---
+
+## S4: Skills Guide
+
+**Bold tools (resume Technical Skills section):**
+Python, SQL (Oracle · Teradata · Athena), AWS (S3 · Glue · Athena · Redshift), PySpark, Airflow
+
+**Must-include skills (ATS match):**
+- Python, SQL, data modeling
+- AWS (Athena, Glue, Redshift, S3), Apache Iceberg
+- Apache Airflow, ETL/ELT
+- Teradata, Oracle DB
+- PySpark, Pandas
+- Grafana or data visualization tools (Plotly/Matplotlib)
+
+**Nice-to-have (include if JD mentions):**
+- Apache Kafka (pipeline depth signal)
+- dbt (not evidenced — do NOT claim; if JD requires, flag to user)
+- Tibco Spotfire (niche — only for Spotfire-specific JDs)
+- Kubernetes (engineering depth signal if JD asks for it)
+
+**Omit:**
+- ELK Stack, Prometheus, Loki (infra monitoring — not analytics signal)
+- Ansible, IaC/CloudFormation (infra — not analytics signal)
+- RPA/UIPath, BDD, Selenium (testing — irrelevant)
+
+**Certifications to highlight:**
+- AWS Certified Solutions Architect – Associate → supporting (confirms AWS stack)
+- Data Engineering with AWS (Udacity) → directly relevant
+- iSAQB CPSA Foundation Level → minor supporting signal
+
+---
+
+## S5: Cover Letter Guide
+
+**Institution type:** Industry — data-driven product company, BI/analytics team, or data consultancy
+
+**Opening hook pattern (generic):**
+> "At Swisscom, I own both sides of the analytics equation: I build and operate the Fulfillment ETL pipelines that feed our Teradata data warehouse, and I deliver the data products and dashboards that our B2B teams rely on for decision-making. [Tie to their specific analytics use case]."
+
+**Opening hook pattern (semiconductor JD):**
+> "My experience at Bosch Semiconductor sits at the intersection of data engineering and semiconductor manufacturing analytics — I built the pipelines that fed defect management and parameter testing teams, owned the analytics platforms those teams relied on, and deployed ML-based image classification that automated defect analysis on the production line. I'd bring that same domain depth and ownership mentality to [Company]."
+
+**Key narrative thread:**
+1. **Analytics platform ownership** — App Owner at Bosch: not just building queries, but owning the analytics software that teams depend on
+2. **Pipeline-to-insight chain** — Fulfillment Component Owner at Swisscom: show the full chain from raw Oracle/Kafka data → Teradata DWH → B2B analytics
+3. **Cloud analytics stack** — AWS migration with Athena/Iceberg/Glue: modern lakehouse architecture for analytics workloads
+4. **Semiconductor domain** (for semi JDs): Defect Management + Parameter Testing + Process Analysis — rare domain expertise in an Analytics Engineer candidate
+
+**"Why them" angle to research:**
+- What business domain are their analytics teams serving? Map to Swisscom (telecom) or Bosch (manufacturing) experience
+- What is their analytics stack? AWS-heavy → your SW-1 migration is directly relevant
+- Do they use dbt? Flag if so — not in your stack, but Airflow/Glue is adjacent
+
+**Avoid:**
+- Leading with infrastructure or DevOps framing
+- Overplaying ML/AI angle (secondary for analytics audience)
+- Mentioning ITIL, SCEDAS, or maritime research context

resume_builder/bundles/bundle_data_engineer.md

@@ -0,0 +1,122 @@
+# Bundle: Staff / Senior Data Engineer
+
+> Target employers: Tech companies, scale-ups, platform teams
+> Tier: 1 — strongest evidence, full portfolio
+> Config key: bundle_data_engineer.md
+
+---
+
+## S1: Role Profile & Priority Matrix
+
+**Positioning:** Dennis is a Staff-level data engineer with 5+ years building production-grade ETL pipelines and data platforms at scale — from Oracle/Teradata DWH ownership at Swisscom (Switzerland's largest telco) to containerized ML inference in a 24/7 semiconductor fab at Bosch. His AWS certification (SAA, active), cloud migration ownership, and Kubernetes-based deployment experience position him as a senior-to-staff candidate across data engineering, data platform, and data infrastructure roles.
+
+**Promotion signal to use:** "Promoted from Senior to Staff Engineer (Engineer IV) at Swisscom, April 2025."
+
+### Priority Matrix
+
+| Priority | Achievement IDs | Rationale |
+|----------|----------------|-----------|
+| HIGH | SW-2, SW-1, SW-3, BS-3, BS-1, BS-2 | Core DE ownership: component owner, AWS migration, K8s/CI/CD, app owner, ML pipelines, data services |
+| MED | SW-4, SW-5, SW-6, BS-4, FC-1, VZ-2, GN-1 | Breadth signals: stakeholder products, DevSecOps, PySpark, ELK PoC, CI/CD, BDD ownership |
+| LOW | FC-2, FC-3, VZ-1, GN-2, GN-3, CA-1 | Earlier career / non-core for this audience |
+
+**2-page resume bullet allocation (typical):**
+- Swisscom: 3–4 bullets (SW-1, SW-2, SW-3, SW-4 or SW-5)
+- Bosch: 3 bullets (BS-1, BS-2, BS-3; +BS-4 if space)
+- Fraunhofer: 1–2 bullets (FC-1 compressed)
+- Vizrt: 1 bullet (VZ-1 + VZ-2 combined)
+- Generali: 1 bullet (GN-1)
+
+---
+
+## S2: Summary Guide
+
+**Headline pattern:**
+> "Staff Data Engineer | AWS · Kafka · Kubernetes | ETL Pipelines, Cloud Migration & Production ML"
+
+**Building blocks** (3–5 phrases that should appear in summaries for this role type):
+- "end-to-end ETL pipeline ownership" or "component ownership of business-critical data pipelines"
+- "cloud migration" or "legacy-to-AWS migration"
+- "Kafka-based event-driven ingestion"
+- "Kubernetes deployment" or "containerized data applications"
+- "AWS Certified Solutions Architect" (cert signal)
+
+**Tone:** Engineer who owns systems, not just builds them. Accountability + delivery. Operator mindset.
+
+**Avoid:**
+- Academic or research framing
+- "Passionate about data" clichés
+- Overemphasizing testing/QA background (earlier career)
+- Listing every tool — focus on the stack that matters for the JD
+
+---
+
+## S3: Achievement Reframing Map
+
+| ID | Default Framing | This Role's Framing | Key Metric / Signal |
+|----|----------------|--------------------|--------------------|
+| SW-2 | Component Owner, Fulfillment ETL | **Lead bullet** — "owned business-critical Fulfillment pipelines end-to-end, on-call SLA, Data Governance compliance" | Component Owner title, on-call accountability |
+| SW-1 | AWS migration | "Migrated legacy Teradata/Oracle ETL stack to AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation)" | Cloud-native stack breadth; Iceberg signals modern data lakehouse |
+| SW-3 | K8s + GitLab CI/CD | "Deployed and operated Python data apps on Kubernetes with GitLab CI/CD in agile DevOps team" | K8s + CI/CD = full DevOps ownership |
+| BS-3 | Application Owner | "Application Owner for semiconductor analytics suite — SLOs, vendor management, training, documentation" | SLO ownership = senior signal |
+| BS-1 | ML inference in fab | "Containerized ML inference (Docker, K8s, Ansible) into 24/7 production; automated image-based defect classification" | Production ML in constrained environment |
+| BS-2 | Data services Oracle/Hadoop | "Built Python/Java/C# data services over OracleDB and Hadoop/ImpalaSQL for semiconductor analysis teams" | Multi-language, enterprise DB breadth |
+| SW-4 | B2B products | "Delivered data products and dashboards for B2B stakeholders; drove process automation" | Stakeholder-facing breadth |
+| BS-4 | ELK PoC | "Delivered anomaly detection PoC: ELK + Kafka, Grafana/Prometheus/Loki monitoring" | Observability initiative |
+
+---
+
+## S4: Skills Guide
+
+**Bold tools (resume Technical Skills section):**
+Python, Kafka, AWS (S3 · Glue · Athena · Redshift · Airflow · CloudFormation), Kubernetes, Teradata
+
+**Must-include skills (ATS match):**
+- Python, SQL, ETL/ELT
+- Apache Kafka, Apache Airflow
+- AWS (S3, Glue, Athena, Redshift), Apache Iceberg
+- Kubernetes, Docker, GitLab CI/CD
+- Teradata, Oracle DB
+- PySpark
+
+**Nice-to-have (include if JD mentions):**
+- SAP BODS, Hadoop/Impala, Step Functions, Lambda
+- Grafana, Prometheus, ELK Stack
+- Ansible, IaC/CloudFormation
+- dbt (not evidenced — do NOT claim if not in JD; omit)
+
+**Omit:**
+- RPA/UIPath, Camunda, IBM ODM (too early-career/non-core)
+- HP Quality Center, Serenity-BDD, JBehave (testing tools — irrelevant)
+- C++, J2EE (legacy — omit unless JD explicitly asks)
+
+**Certifications to highlight:**
+- AWS Certified Solutions Architect – Associate (active, 2024–2027) → HIGH value for this role type
+- Data Engineering with AWS (Udacity, 2026) → supporting signal
+- iSAQB CPSA Foundation Level → supporting (architecture awareness)
+
+---
+
+## S5: Cover Letter Guide
+
+**Institution type:** Industry — tech company, scale-up, or enterprise platform team
+
+**Opening hook pattern:**
+> "As a Staff Data Engineer at Swisscom — Switzerland's largest telco — I currently own the business-critical Fulfillment ETL pipelines that feed our data warehouse, while simultaneously leading the migration of our legacy stack to a cloud-native AWS architecture. [Tie to their specific need / JD signal]."
+
+**Key narrative thread:**
+1. **Ownership at scale** — Component Owner at Swisscom, Application Owner at Bosch: not just building pipelines, but running them in production with SLA accountability
+2. **Cloud-native evolution** — AWS migration (Athena/Iceberg, Glue, Airflow, CloudFormation): led the transition, not just participated
+3. **Production ML integration** — Bosch: ML inference containerized into 24/7 fab; demonstrates that "data engineer who can own the ML data layer"
+4. **Consistent seniority arc** — Bosch promotion (mid → Senior), Swisscom promotion (Senior → Staff)
+
+**"Why them" angle to research:**
+- What is their data stack? Match Kafka/Airflow/AWS overlaps explicitly
+- Are they migrating to cloud or lakehouse architecture? → Your SW-1 experience is directly relevant
+- Do they operate pipelines in production SLAs? → Component Owner + on-call duty is your signal
+
+**Avoid:**
+- Starting with "I am passionate about data"
+- Listing all tools in paragraph form
+- Mentioning Bundeswehr unless specifically relevant (leadership angle for management-adjacent roles)
+- Overplaying test automation background

resume_builder/bundles/bundle_data_platform.md

@@ -0,0 +1,127 @@
+# Bundle: Data Platform / Infra
+
+> Target employers: Cloud-first companies, AWS-heavy orgs
+> Tier: 3 — viable with careful framing
+> Config key: bundle_data_platform.md
+
+---
+
+## S1: Role Profile & Priority Matrix
+
+**Positioning:** Dennis's data platform and infrastructure experience is woven throughout his career rather than being a dedicated "platform engineer" role — but the evidence is substantive: Kubernetes ownership at two employers, AWS migration with CloudFormation/IaC, GitLab CI/CD automation, Docker containerization of ML workloads, observability stack (ELK + Grafana + Prometheus), and 3 consecutive years as Swisscom Security Champion (DevSecOps). Position as "Data Engineer with strong platform and infrastructure ownership" rather than a dedicated Platform/SRE/DevOps role.
+
+**Note on Tier 3:** This bundle is viable but slightly less natural than Tier 1/2. The gap is: Dennis doesn't have a dedicated platform engineering title, and his infrastructure work is in service of data pipelines rather than standalone infrastructure. Frame accordingly — emphasize that his platform skills are production-proven, not academic.
+
+### Priority Matrix
+
+| Priority | Achievement IDs | Rationale |
+|----------|----------------|-----------|
+| HIGH | SW-3, SW-1, SW-2, BS-1, BS-2, BS-3, BS-4, SW-5 | K8s/GitLab, AWS/IaC, pipeline ownership, ML containerization, data services, ELK observability, DevSecOps |
+| MED | SW-4, SW-6, FC-1, FC-3, VZ-2, BS-5 | Automation, PySpark, CI/CD initiative, microservices, quality gates |
+| LOW | FC-2, VZ-1, GN-1, GN-2, CA-1 | Non-platform signals |
+
+**2-page resume bullet allocation (typical):**
+- Swisscom: 3–4 bullets (SW-3, SW-1, SW-2, SW-5)
+- Bosch: 3 bullets (BS-1, BS-2 or BS-3, BS-4)
+- Fraunhofer: 1 bullet (FC-1 — CI/CD initiative)
+- Vizrt: 1 bullet (VZ-2 — quality gates in CI/CD)
+- Generali: 1 bullet (GN-1 or omit)
+
+---
+
+## S2: Summary Guide
+
+**Headline pattern:**
+> "Data Platform Engineer | Kubernetes · AWS · Kafka | Cloud-Native Data Infrastructure, IaC & DevSecOps"
+
+**Building blocks:**
+- "cloud-native data infrastructure" or "data platform ownership"
+- "Kubernetes-based containerized pipeline deployment"
+- "AWS IaC (CloudFormation)" — infrastructure-as-code signal
+- "AWS migration" — hands-on cloud platform experience
+- "DevSecOps / Security Champion" — security-aware platform engineer
+- "ELK + Grafana + Prometheus observability stack"
+
+**Tone:** Infrastructure-minded engineer who thinks about reliability, observability, and security — not just data throughput. Platform thinking embedded in data work.
+
+**Avoid:**
+- Leading with analytics or BI framing
+- Overemphasizing test automation background
+- Positioning as SRE or pure DevOps (the role was data engineering with platform ownership)
+
+---
+
+## S3: Achievement Reframing Map
+
+| ID | Default Framing | This Role's Framing | Key Metric / Signal |
+|----|----------------|--------------------|--------------------|
+| SW-3 | K8s + GitLab | **Lead bullet** — "Deployed and operated Python data applications on Kubernetes with GitLab CI/CD; drove infrastructure automation in agile DevOps team" | K8s + CI/CD ownership = core platform signal |
+| SW-1 | AWS migration | "Migrated legacy ETL stack to cloud-native AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) — full IaC stack provisioned via CloudFormation" | CloudFormation/IaC + full AWS service breadth |
+| SW-2 | Component Owner | "Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) — platform reliability, Data Governance compliance, 2nd/3rd-level support and on-call duty" | Platform SLA + on-call = reliability engineer signal |
+| BS-1 | ML inference | "Containerized and orchestrated ML inference (Docker, K8s, Ansible) into 24/7 semiconductor production — zero-downtime constrained deployment" | Production-grade containerization under hardest constraints |
+| BS-4 | ELK PoC | "Designed and delivered observability stack: ELK + Kafka, Grafana dashboards, Prometheus metrics, Loki log aggregation — full monitoring suite for manufacturing infrastructure" | Full observability stack implementation |
+| SW-5 | Security Champion | "Swisscom Security Champion ×3 (2023–2026) — DevSecOps ownership, security compliance, risk monitoring and deviation tracking for Data Lake team" | Security ownership in platform context |
+| BS-2 | Data services | "Built multi-language data services (Python/Java/C#) over OracleDB and Hadoop/ImpalaSQL — platform-layer data access for semiconductor analysis teams" | Enterprise DB + Hadoop infrastructure |
+| BS-3 | App Owner | "Application Owner for semiconductor analytics platform — SLOs, reliability, vendor management, on-call coverage" | Platform SLA ownership |
+| FC-1 | CI/CD initiative | "Independently introduced Jenkins CI/CD pipeline with quality gates at Fraunhofer CML — first build automation adopted by the research team" | Initiative: built CI/CD from zero |
+
+---
+
+## S4: Skills Guide
+
+**Bold tools (resume Technical Skills section):**
+Kubernetes, Docker, AWS (S3 · Glue · Athena · Redshift · CloudFormation), Kafka, GitLab CI/CD
+
+**Must-include skills (ATS match):**
+- Kubernetes, Docker, Ansible
+- AWS (S3, Glue, Athena, Redshift, CloudFormation, Airflow), Apache Iceberg
+- GitLab CI/CD, Jenkins
+- Kafka, Apache Airflow
+- Python, SQL
+- ELK Stack, Grafana, Prometheus
+- IaC / CloudFormation
+- DevSecOps
+
+**Nice-to-have (include if JD mentions):**
+- Terraform (not evidenced — do NOT claim; flag if JD requires)
+- Loki (log aggregation — from Bosch PoC)
+- PySpark (distributed processing on platform)
+- Ansible (Bosch ML orchestration)
+- Oracle DB, Teradata (enterprise data platform experience)
+
+**Omit:**
+- BDD, Selenium, HP Quality Center, UIPath (testing — irrelevant)
+- Tibco Spotfire, SAP BODS (application tools — irrelevant)
+- RPA/Camunda (process automation — irrelevant)
+
+**Certifications to highlight:**
+- AWS Certified Solutions Architect – Associate → HIGH (platform credibility, architecture knowledge)
+- Data Engineering with AWS → supporting
+- iSAQB CPSA Foundation Level → MED (software architecture — relevant for platform design decisions)
+
+---
+
+## S5: Cover Letter Guide
+
+**Institution type:** Cloud-first tech company, scale-up with AWS-heavy stack, enterprise platform team, or data infrastructure consultancy
+
+**Opening hook pattern:**
+> "Across my career at Swisscom and Bosch, I've owned data infrastructure at two ends of the spectrum: migrating Swisscom's legacy ETL stack to a cloud-native AWS platform (CloudFormation, Glue, Athena with Iceberg, Airflow) while operating Kubernetes-deployed Python applications with GitLab CI/CD — and containerizing ML inference into a 24/7 semiconductor production line at Bosch using Docker, Kubernetes, and Ansible. In both cases, the infrastructure had to be production-grade with no tolerance for downtime. [Tie to their platform challenge]."
+
+**Key narrative thread:**
+1. **Production Kubernetes** — SW-3 + BS-1: K8s at two employers, in different contexts (data apps at Swisscom, ML inference at Bosch). Cross-employer K8s ownership is a strong signal.
+2. **Full AWS platform stack** — SW-1: Not just using one AWS service — migrating an entire ETL infrastructure to AWS with CloudFormation/IaC shows platform-level thinking.
+3. **Observability initiative** — BS-4: Self-initiated ELK + Prometheus + Grafana PoC shows platform engineer mindset (monitoring is not optional).
+4. **Security ownership** — SW-5: Security Champion ×3 = DevSecOps embedded in platform work, not an afterthought.
+
+**"Why them" angle to research:**
+- What is their cloud stack? If AWS-heavy → your SAA cert + migration experience is directly relevant
+- Do they use Kubernetes in production? → Cross-employer K8s experience is the signal
+- Are they building their data platform from scratch vs. maintaining existing? → Tailor SW-1 (migration) vs. BS-4 (observability initiative) accordingly
+- Terraform vs. CloudFormation? → Note that your experience is CloudFormation; Terraform familiarity may need bridging
+
+**Avoid:**
+- Leading with analytics or BI outcomes (platform audience cares about reliability and infrastructure)
+- Claiming SRE/pure DevOps title (you were a data engineer with platform ownership)
+- Overstating Terraform/Helm experience (not confirmed — do not claim)
+- Mentioning SCEDAS, maritime research, BDD, or RPA

resume_builder/bundles/bundle_ml_ai_engineer.md

@@ -0,0 +1,127 @@
+# Bundle: ML / AI Engineer
+
+> Target employers: AI product companies, R&D teams
+> Tier: 2 — strong with targeted emphasis
+> Config key: bundle_ml_ai_engineer.md
+
+---
+
+## S1: Role Profile & Priority Matrix
+
+**Positioning:** Dennis is an ML Engineer who specializes in the *infrastructure and deployment* side of ML — not ML research or model training from scratch. His flagship signal is containerizing and orchestrating ML inference into a 24/7 semiconductor production environment (Bosch) — a production ML deployment in one of the most demanding operational contexts imaginable. This is paired with NLP/speech recognition research (Fraunhofer ARTUS), early deep learning exposure (IBM AI Engineering, Udacity AI for Trading), and current AWS/Kubernetes ownership at Swisscom.
+
+**Honest framing:** Dennis is strongest in MLOps and ML infrastructure. He has real but limited model development experience (ARTUS NLP, image classification at Bosch). Do NOT position as a research ML scientist or model architect. DO position as "ML Engineer who can own the full deployment lifecycle and build reliable production ML infrastructure."
+
+**Key differentiator:** Production ML in a 24/7 constrained environment (24h uptime, no deployment windows, hardware-in-the-loop) is a rare and credible signal.
+
+### Priority Matrix
+
+| Priority | Achievement IDs | Rationale |
+|----------|----------------|-----------|
+| HIGH | BS-1, SW-3, SW-1, SW-2 | Production ML deployment, K8s infrastructure, AWS data lake, pipeline ownership |
+| MED | FC-2, SW-5, BS-2, BS-3, BS-4 | NLP research, DevSecOps, data services, app ownership, observability |
+| LOW | SW-4, FC-1, FC-3, VZ-1, VZ-2, GN-1, GN-2 | Not core ML signal |
+
+**2-page resume bullet allocation (typical):**
+- Swisscom: 3 bullets (SW-3, SW-1, SW-2)
+- Bosch: 3 bullets (BS-1, BS-2 or BS-3, BS-4)
+- Fraunhofer: 1–2 bullets (FC-2 ARTUS; FC-1 if CI/CD is relevant)
+- Vizrt: 1 bullet (compressed; or omit)
+- Generali: 1 bullet (GN-1 or omit)
+
+---
+
+## S2: Summary Guide
+
+**Headline pattern:**
+> "ML Engineer | MLOps · Kubernetes · AWS | Production ML Deployment, Data Infrastructure & Applied NLP"
+
+**Building blocks:**
+- "production ML deployment" or "ML inference at scale"
+- "containerized ML pipelines" (Docker, Kubernetes, Ansible)
+- "ML infrastructure and data platform" (AWS, Airflow, Kafka)
+- "applied NLP / speech recognition" (Fraunhofer ARTUS — hedged)
+- "AWS Certified Solutions Architect" (infra credibility)
+
+**Tone:** Pragmatic ML engineer who ships ML to production. Operational rigor. Infrastructure-first mindset. Not a researcher — an engineer who makes ML run reliably.
+
+**Avoid:**
+- Claiming deep model research or novel architecture contributions
+- Overstating NLP depth (ARTUS was contributing developer, not solo)
+- Leading with test automation or BI/analytics framing
+- "Passionate about AI" clichés
+
+---
+
+## S3: Achievement Reframing Map
+
+| ID | Default Framing | This Role's Framing | Key Metric / Signal |
+|----|----------------|--------------------|--------------------|
+| BS-1 | ML inference containerization | **LEAD bullet** — "Designed and deployed ML inference pipeline (Docker, K8s, Ansible) into 24/7 semiconductor fab; automated image-based defect classification" | Production ML in constrained 24/7 environment |
+| SW-3 | K8s + GitLab CI/CD | "Deployed and operated ML-ready Python applications on Kubernetes with GitLab CI/CD automation — production-grade containerized delivery" | K8s ownership = MLOps infrastructure |
+| SW-1 | AWS migration | "Built cloud-native data infrastructure on AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow) — the scalable data layer that ML models depend on" | AWS data lake for ML workloads |
+| SW-2 | Component Owner | "Owned business-critical ETL pipelines (Oracle/Kafka → Teradata) — reliable data supply for downstream ML and analytics" | Data reliability for ML input |
+| FC-2 | ARTUS NLP | "Contributed ML and speech recognition components to ARTUS — Fraunhofer research project targeting automatic sea rescue transcription" | Applied NLP in safety-critical domain |
+| BS-4 | ELK PoC | "Delivered anomaly detection PoC: ELK + Kafka pipeline with Grafana/Prometheus monitoring — ML-adjacent signal processing" | Anomaly detection / observability |
+| SW-5 | Security Champion | "Swisscom Security Champion ×3 — security and compliance ownership for ML pipeline data governance and DevSecOps" | Security in ML data pipeline context |
+| BS-2 | Data services | "Built data services (Python/Java/C#) over OracleDB and Hadoop enabling ML model input pipelines in semiconductor manufacturing" | Data infrastructure for ML |
+
+---
+
+## S4: Skills Guide
+
+**Bold tools (resume Technical Skills section):**
+Python, Kubernetes, Docker, AWS (S3 · Glue · Athena · Airflow), Kafka, PyTorch · Scikit-learn
+
+**Must-include skills (ATS match):**
+- Python, MLOps, ML inference deployment
+- Docker, Kubernetes, Ansible
+- AWS (S3, Glue, Athena, Redshift, Airflow), Apache Iceberg
+- Apache Kafka
+- PyTorch, Scikit-learn, Pandas, NumPy
+- CI/CD (GitLab, Jenkins)
+
+**Nice-to-have (include if JD mentions):**
+- NLP, speech recognition (hedge — contributing developer at Fraunhofer)
+- TensorFlow/Keras (IBM cert — familiarity level; note if JD requires)
+- PySpark (Swisscom — Spark ML adjacent)
+- Grafana, Prometheus (monitoring ML systems)
+- Apache Spark ML (familiarity via cert)
+
+**Omit:**
+- BDD, Selenium, HP Quality Center (testing — irrelevant)
+- RPA/UIPath, Camunda (process automation — irrelevant)
+- Tibco Spotfire (BI tool — irrelevant)
+- SAP BODS (legacy — omit for ML audience)
+
+**Certifications to highlight:**
+- AWS Certified Solutions Architect – Associate → HIGH (infra credibility for MLOps)
+- AI for Trading Nanodegree (Udacity) → MED (quantitative ML exposure)
+- IBM AI Engineering Specialization → MED (TensorFlow, Keras, Spark ML depth signal)
+- Data Engineering with AWS → supporting
+
+---
+
+## S5: Cover Letter Guide
+
+**Institution type:** AI product company, applied ML team, or R&D engineering team (not pure research lab)
+
+**Opening hook pattern:**
+> "My most defining ML engineering project was at Bosch Semiconductor in Dresden: I designed and deployed the strategy for containerizing ML inference models into a 24/7 wafer production line — orchestrating with Kubernetes and Ansible, running Docker-containerized image classification models continuously against production data, with no downtime tolerance. That production constraint is what sharpens MLOps thinking. [Tie to their ML deployment challenge]."
+
+**Key narrative thread:**
+1. **Production ML ownership** — BS-1: not a research demo, not a notebook — ML running uninterrupted in a semiconductor fab. Establish this credibility early.
+2. **Infrastructure that ML depends on** — SW-1 + SW-2: the AWS data lake and reliable Kafka/ETL pipelines that feed ML models. Shows full-stack ML engineering thinking.
+3. **Applied ML research background** — FC-2: Fraunhofer ARTUS NLP project establishes early research exposure (hedge: contributing developer)
+4. **Certs as credibility** — AWS SAA + IBM AI Engineering + Udacity AI for Trading: show structured ML and cloud learning
+
+**"Why them" angle to research:**
+- What ML problems are they solving? Map to defect detection (vision ML), NLP (Fraunhofer), or infrastructure (Swisscom)
+- How mature is their MLOps? If early-stage → your Bosch experience building from scratch is directly relevant
+- Are they AWS-heavy? → Your migration and cert are strong signals
+
+**Avoid:**
+- Claiming to be a research ML scientist (your strength is deployment and infrastructure)
+- Overstating NLP depth ("contributed to" not "led")
+- Listing PyTorch/Keras without framing (cert exposure, not daily production use)
+- Mentioning SCEDAS, maritime research, BDD, or RPA context