dennisthiessenandClaude Opus 5.5
504fa373f7
Application - Kongsberg Discovery / Seatex, Lead Engineer DevOps and Test Infrastructure (Trondheim): resume, cover letter (+ paste text), verbatim JD, session and critique. Submitted 2026-10-08 via EasyCruit as a deliberate cold application on a user override of the embedded-SW hard gate (Evidence Fit 69, Document Quality 84). Recorded in applications.json and decisions.json. - Google Senior SWE Full Stack Commerce: session and verbatim JD from 2026-10-03 (no-go on the frontend gap, held, not submitted). Knowledge base - claims.json: ED-1 (embedded/robotics coursework, none since studies), SW-9 (current unit testing, pytest), new skills (pytest, unit testing, embedded systems (coursework), VHDL, robotics (coursework)), and Jenkins and GitLab CI/CD registered from existing GN-1/FC-1/BS-6/SW-3 scopes. - Earlier uncommitted KB work: 2026-09-18 Python frameworks / enterprise SaaS clarification, 2026-10-03 data-product design / no-frontend clarification, 2026-10-06 working-model rule (3 homeoffice days baseline) in config.md and application_strategy.md, plus related experience/bundle/taxonomy updates. - CLAUDE.md corrections log: 2026-10-08 entry for the three canonical gaps. Scout - 2026-10-08 run triaged by JD read: NVIDIA DGX Cloud Senior Production Engineer, Google SRE III Prod AI / Senior SRE / Staff Shopping Graph and Amazon RIVR DevOps logged as maybe; GenAI FDE family, Kraken AI Platform, PostFinance DevOps and five Kdo Cy reqs logged as skip with reasons. Housekeeping - .gitignore: ignore .cache/ and raw HTML page dumps under output/. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
119 lines
7.4 KiB
Markdown
119 lines
7.4 KiB
Markdown
# Skills Taxonomy — Evidence-First
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> Canonical authority: `resume_builder/canonical/claims.json`.
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> This file helps select and group skills; it may not promote a skill beyond its canonical evidence level.
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## Evidence Levels
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| Level | Meaning | Output rule |
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|---|---|---|
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| Production — current | Used in current professional delivery | May appear plainly when relevant |
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| Production — historical | Shipped professionally, but not current | Include with role/date context when recency matters |
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| Hands-on — current | Used directly, but without verified production ownership | Use precise verbs such as used, configured or integrated |
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| Project / proof of concept | Used in a bounded PoC | Label the PoC; never imply platform-scale operation |
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| Certification / coursework | Learned through formal study | Keep in certification context; not professional experience |
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| Unverified / never used | No reliable evidence | Do not include |
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Do not use Expert/Proficient/Familiar labels in resumes. Evidence and recency are more useful than self-ratings.
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## Current Production Core
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| Skill | Evidence | Typical use |
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|---|---|---|
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| Python | Swisscom pipelines/apps; prior Bosch/Vizrt work | Always for data/platform roles |
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| SQL | Swisscom and prior data roles | Always for data roles |
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| PySpark | Swisscom current work | When distributed processing is relevant |
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| Apache Kafka | Swisscom production ingestion | Data/event-driven roles |
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| Apache Airflow | Swisscom AWS migration scope | Orchestration/data roles |
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| AWS | Swisscom production work; SAA certification | AWS-relevant roles |
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| S3, Glue, Athena, Iceberg, Redshift | Swisscom owned-domain migration/data products | Name only relevant services |
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| CloudFormation / IaC | Swisscom production provisioning | Say CloudFormation; never substitute Terraform |
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| Kubernetes, Docker | Swisscom application delivery; Bosch ML integration | Production platform/MLOps roles |
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| GitLab CI/CD | Swisscom delivery | Platform and engineering roles |
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| Oracle, Teradata | Swisscom pipelines | Data roles when relevant |
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## Historical Production Evidence
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| Skill | Evidence | Constraint |
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|---|---|---|
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| Java | Bosch, Fraunhofer, Generali | Historical; do not imply current daily use |
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| C# | Bosch and Fraunhofer | Historical; strong when Spotfire/.NET is relevant |
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| C++ | Vizrt distributed backend | Limited historical evidence |
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| JavaScript / Express.js | Fraunhofer MISSION | Historical and bounded; TypeScript is unverified |
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| Hadoop / Impala | Bosch data services | Historical production context |
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| Ansible | Bosch ML integration | Historical production context |
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| Jenkins | Fraunhofer and Generali | Historical production context |
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| BDD, Selenium, JBehave | Generali | Earlier-career testing evidence |
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| TIBCO Spotfire | Bosch co-ownership and C# extensions | Preserve co-ownership |
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## ML, AI and Observability
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| Skill | Evidence level | Safe framing |
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|---|---|---|
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| ML inference deployment | Production — historical | Integrated containerized inference into a 24/7 Bosch fab |
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| Image classification | Production application context | Worked on inference integration; model-training ownership not verified |
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| MLOps | Bounded production evidence | Use only when defined as deployment/operation, not full model lifecycle |
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| NLP / speech recognition | Research-project contribution | Contributed components at Fraunhofer; no publication/model ownership |
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| ELK, Kafka anomaly detection | Proof of concept | Always retain the PoC label |
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| Grafana, Prometheus, Loki | Proof-of-concept/monitoring context | Do not imply enterprise observability ownership |
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| LiteLLM | Hands-on — current | LLM API gateway use/integration; no serving-platform ownership |
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| Domain-grounded assistants/custom GPTs | Hands-on — current | Configured with curated knowledge; no fine-tuning or formal evaluation |
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| Copilot, Kiro | Hands-on — current | AI-assisted engineering tools, not LLM product engineering |
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| Linux administration | Professional 2020–2022 (BS-6) + personal and current (PP-3) | Employer-side use ended Dec 2022; PP-3 is a personal Debian server and must be named as such. Not evidence of physical-datacenter work |
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| Ansible | Professional — intensive at Bosch (BS-6) | Configuration management and IaC, including self-written extensions. Does NOT license Terraform. **GitOps is forbidden** — push-based execution, no reconciliation agent |
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| Bash / shell scripting | Professional-historical + personal-current (BS-6, PP-3) | Added to `claims.json` 2026-09-05. Does NOT license Groovy, nor systems/kernel-level claims |
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## Personal-Project Framework Experience
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| Skill | Evidence level | Safe framing |
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| Django, Flask, FastAPI | Project / proof of concept — private/hobby projects (PP-4) | Used in personal projects, user-confirmed 2026-09-18. Never used professionally. Project details, dates and depth are not established; do not attach to an employer or a named project without confirmation. |
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## Certification-Only Signals
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| Skill | Evidence |
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|---|---|
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| TensorFlow / Keras | IBM AI Engineering coursework |
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| PyTorch | **Certification-backed** (IBM AI Engineering, 4 Jun 2020, verify code 3ZBZFVAL6A34). `claims.json` output policy is `certification-context-only`: list only beside the certificate |
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| Spark ML | Coursework context only unless professional evidence is added |
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| AI for Trading / quantitative ML | Udacity/WorldQuant Nanodegree |
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## Forbidden Until New Evidence Is Added
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- LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI or Semantic Kernel
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- Azure, Azure ML, Azure OpenAI or AKS
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- GCP, BigQuery, Dataflow or Flume hands-on experience
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- Terraform
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- Formal model or LLM evaluation
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- LLM fine-tuning, red-teaming or model-training ownership
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- Enterprise SaaS/SSO administration — user confirmed 2026-09-18 that he has not performed it. Do not infer enterprise identity administration from personal hosting or secrets-management work.
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- TypeScript
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- Frontend/UI development — user answered no to frontend experience on 2026-10-03. Backend JavaScript and Selenium UI testing do not establish frontend development; do not claim Full Stack expertise.
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- Petabyte-scale ownership
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## Certifications
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| Certification | Issuer | Year/status |
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| AWS Certified Solutions Architect — Associate | AWS | 2024; active to Sep 2027 |
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| Data Engineering with AWS Nanodegree | Udacity | 2026 |
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| iSAQB CPSA — Foundation | iSAQB | 2016; no expiry |
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| ITIL Foundation | PEOPLECERT / AXELOS | 2016; no expiry |
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| AI for Trading Nanodegree | Udacity / WorldQuant | 2021 |
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| IBM AI Engineering Specialization | IBM / Coursera | **Completed 4 Jun 2020**, no expiry, verify code 3ZBZFVAL6A34 (certificate read 2026-08-27). Explicitly non-credit — never present as an academic qualification |
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The Swisscom Security Champion assignment is not a certification and does not belong in this table.
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## Resume Grouping
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Use 4--6 compact lines, selected for the JD. A normal International Tech grouping is:
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1. Languages: Python, SQL; selected historical languages only when required.
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2. Data: Kafka, Airflow, PySpark, Oracle/Teradata, data products and governance.
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3. Cloud/platform: AWS services, CloudFormation, Kubernetes, Docker, GitLab CI/CD.
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4. ML/operations: ML inference deployment and bounded observability evidence.
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5. Certifications: one line, only the most relevant credentials.
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Never add a skill only to mirror a JD. Every listed skill must have a canonical evidence level and an interview-ready example.
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