Quality engineer (ASQ-CQE, 15+ years in regulated hardware manufacturing) who deploys and operates private LLM infrastructure: multi-node serving, evaluation gates, silent-failure monitoring and disaster recovery, run with the same discipline as a production line.
I build with AI coding agents (Claude Code, Codex, GitHub Copilot CLI). I set the architecture, constraints and acceptance criteria, then review, test, deploy and operate what they write.
| Repo | What it shows |
|---|---|
| onprem-llm-stack | Two NVIDIA GB10 nodes serving large quantized models (MiniMax M2.7, Qwen3.5-122B, Qwen3.8-Flash-Next) tensor-parallel on vLLM / SGLang / Atlas, behind a LiteLLM gateway and OpenWebUI with SSO. Includes an A/B evaluation harness that caught a faster recipe failing long-context retrieval, a watchdog for a silent NaN-logits fault (detect → contain → correct → escalate), an Ansible rebuild with a written DR runbook, a measured trial of Cohere Command A+ (218B MoE, TP=2), and a tool-call eval of the Home Assistant voice agent |
| audio2report | Dual-mic meeting recordings → deduplicated transcript → LLM audit report, fully local (WhisperX, pluggable local LLM backends, CPU/GPU Docker images, CI). A work version has been used on four audits |
| LLM-chatbot | RAG over PDF manuals: hybrid retrieval (Qdrant dense + OpenSearch BM25, reciprocal-rank fusion) and cross-encoder reranking, with Cohere Command R7B via Ollama and page-level citations |
| Repo | What it shows | Headline |
|---|---|---|
| wafer-defect-classifier | 9-class WM-811K wafer maps: focal loss + CBAM, temperature-scaled calibration, Grad-CAM++ | macro-F1 0.92, ECE 0.003 |
| wafer-ssl | SimCLR pretraining on 638k unlabeled maps + 4-seed ensemble, with an ablation attributing the gain | macro-F1 0.94 |
| wafer-mixed | Multi-label mixed-defect classification (MixedWM38), transfer study, cost-weighted per-label thresholds | macro-F1 0.98 |
| wafer-rootcause | Simulated MES in DuckDB; SQL commonality analysis (z-tests + BH-FDR) recovers planted equipment faults | precision@1 ~0.9–1.0 |
| wafer-deploy | FastAPI serving with a bit-for-bit parity test, three drift monitors, hysteresis retrain trigger, Prometheus + Grafana | scored detection latency and false-alarm rate |
Negative results stay in the docs. For example, a pseudo-labeling regression was rolled back and a loss-function bug is written up as a post-mortem.