dennisthiessenandClaude Opus 5
ce5b7f511f
The review's key point is that the error ran BOTH ways. Overstatement, narrowed to canonical scope: - BS-1 variants claimed "eliminated manual wafer inspection", "no manual intervention" and "unattended quality monitoring". Canonical BS-1 supports a qualitative reduction whose size is unverified. Reworded; the scope limit is now stated inline where the variants are written. - bundle_data_platform called BS-1 a "zero-downtime constrained deployment". Integration into a fab that runs continuously is not a measured zero-downtime property. - experience_swisscom carried "grounded retrieval" as a skill and ATS keyword while SW-8 records retrieval implementation as explicitly unknown, and its reframing note invited "evaluation discipline" although formal model evaluation is a globally forbidden pattern. Both removed. - PyTorch sat in bundle_ml_ai_engineer as a bold tool and an ATS must-include with no certification context - the exact way the context gets lost. Undervaluation, the half that quietly costs him roles: - BS-6 (Linux administration, intensive Ansible incl. self-written extensions) has been canonical since 2026-08-27 but appeared in NO bundle, so the normal selection paths could not see it. Added to the data-platform and data-engineer bundles with its currency limit (employer-side ended Dec 2022; PP-3 is personal) and the standing GitOps prohibition. - Linux, Ansible and Bash were missing from skills_taxonomy entirely. - skills_taxonomy still said the IBM certification year was "not recorded" and that PyTorch needed verification; both were settled on 2026-08-27. Not rewritten, deliberately: the NVIDIA and RUAG cover letters state the production line could not be stopped. That exceeds the canonical record but is plausible and both are already submitted. Recorded as an OPEN ITEM in experience_bosch.md to be confirmed with the user before any reuse. Validator PASS, 31/31 tests. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qej5SBvhcbGXHwLqNWiskM
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Skills Taxonomy — Evidence-First
Canonical authority:
resume_builder/canonical/claims.json. This file helps select and group skills; it may not promote a skill beyond its canonical evidence level.
Evidence Levels
| Level | Meaning | Output rule |
|---|---|---|
| Production — current | Used in current professional delivery | May appear plainly when relevant |
| Production — historical | Shipped professionally, but not current | Include with role/date context when recency matters |
| Hands-on — current | Used directly, but without verified production ownership | Use precise verbs such as used, configured or integrated |
| Project / proof of concept | Used in a bounded PoC | Label the PoC; never imply platform-scale operation |
| Certification / coursework | Learned through formal study | Keep in certification context; not professional experience |
| Unverified / never used | No reliable evidence | Do not include |
Do not use Expert/Proficient/Familiar labels in resumes. Evidence and recency are more useful than self-ratings.
Current Production Core
| Skill | Evidence | Typical use |
|---|---|---|
| Python | Swisscom pipelines/apps; prior Bosch/Vizrt work | Always for data/platform roles |
| SQL | Swisscom and prior data roles | Always for data roles |
| PySpark | Swisscom current work | When distributed processing is relevant |
| Apache Kafka | Swisscom production ingestion | Data/event-driven roles |
| Apache Airflow | Swisscom AWS migration scope | Orchestration/data roles |
| AWS | Swisscom production work; SAA certification | AWS-relevant roles |
| S3, Glue, Athena, Iceberg, Redshift | Swisscom owned-domain migration/data products | Name only relevant services |
| CloudFormation / IaC | Swisscom production provisioning | Say CloudFormation; never substitute Terraform |
| Kubernetes, Docker | Swisscom application delivery; Bosch ML integration | Production platform/MLOps roles |
| GitLab CI/CD | Swisscom delivery | Platform and engineering roles |
| Oracle, Teradata | Swisscom pipelines | Data roles when relevant |
Historical Production Evidence
| Skill | Evidence | Constraint |
|---|---|---|
| Java | Bosch, Fraunhofer, Generali | Historical; do not imply current daily use |
| C# | Bosch and Fraunhofer | Historical; strong when Spotfire/.NET is relevant |
| C++ | Vizrt distributed backend | Limited historical evidence |
| JavaScript / Express.js | Fraunhofer MISSION | Historical and bounded; TypeScript is unverified |
| Hadoop / Impala | Bosch data services | Historical production context |
| Ansible | Bosch ML integration | Historical production context |
| Jenkins | Fraunhofer and Generali | Historical production context |
| BDD, Selenium, JBehave | Generali | Earlier-career testing evidence |
| TIBCO Spotfire | Bosch co-ownership and C# extensions | Preserve co-ownership |
ML, AI and Observability
| Skill | Evidence level | Safe framing |
|---|---|---|
| ML inference deployment | Production — historical | Integrated containerized inference into a 24/7 Bosch fab |
| Image classification | Production application context | Worked on inference integration; model-training ownership not verified |
| MLOps | Bounded production evidence | Use only when defined as deployment/operation, not full model lifecycle |
| NLP / speech recognition | Research-project contribution | Contributed components at Fraunhofer; no publication/model ownership |
| ELK, Kafka anomaly detection | Proof of concept | Always retain the PoC label |
| Grafana, Prometheus, Loki | Proof-of-concept/monitoring context | Do not imply enterprise observability ownership |
| LiteLLM | Hands-on — current | LLM API gateway use/integration; no serving-platform ownership |
| Domain-grounded assistants/custom GPTs | Hands-on — current | Configured with curated knowledge; no fine-tuning or formal evaluation |
| Copilot, Kiro | Hands-on — current | AI-assisted engineering tools, not LLM product engineering |
| 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 |
| 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 |
| 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 |
Certification-Only Signals
| Skill | Evidence |
|---|---|
| TensorFlow / Keras | IBM AI Engineering coursework |
| 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 |
| Spark ML | Coursework context only unless professional evidence is added |
| AI for Trading / quantitative ML | Udacity/WorldQuant Nanodegree |
Forbidden Until New Evidence Is Added
- LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI or Semantic Kernel
- Azure, Azure ML, Azure OpenAI or AKS
- GCP, BigQuery, Dataflow or Flume hands-on experience
- Terraform
- Formal model or LLM evaluation
- LLM fine-tuning, red-teaming or model-training ownership
- FastAPI, Flask or Django
- TypeScript
- Petabyte-scale ownership
Certifications
| Certification | Issuer | Year/status |
|---|---|---|
| AWS Certified Solutions Architect — Associate | AWS | 2024; active to Sep 2027 |
| Data Engineering with AWS Nanodegree | Udacity | 2026 |
| iSAQB CPSA — Foundation | iSAQB | 2016; no expiry |
| ITIL Foundation | PEOPLECERT / AXELOS | 2016; no expiry |
| AI for Trading Nanodegree | Udacity / WorldQuant | 2021 |
| 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 |
The Swisscom Security Champion assignment is not a certification and does not belong in this table.
Resume Grouping
Use 4--6 compact lines, selected for the JD. A normal International Tech grouping is:
- Languages: Python, SQL; selected historical languages only when required.
- Data: Kafka, Airflow, PySpark, Oracle/Teradata, data products and governance.
- Cloud/platform: AWS services, CloudFormation, Kubernetes, Docker, GitLab CI/CD.
- ML/operations: ML inference deployment and bounded observability evidence.
- Certifications: one line, only the most relevant credentials.
Never add a skill only to mirror a JD. Every listed skill must have a canonical evidence level and an interview-ready example.