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chore(applications): archive BIS + NATO submitted packages, ignore tmp/

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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 <noreply@anthropic.com>
This commit is contained in:
dennisthiessen
2026-07-18 21:03:45 +02:00
co-authored by Claude Sonnet 5
parent 3ad39117d6
commit d0525cc437
18 changed files with 1766 additions and 1 deletions
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resume_builder/experience/experience_swisscom.md
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@@ -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 |