6 +years only AI Engineer RAG & LLM Systems FastAPI LangChain PyTorc

• Build the ingestion and chunking pipeline for plan notes, legends, installation details and specifications, and keep the index versioned alongside the vision model releases
• Implement hybrid retrieval combining semantic and keyword search, so exact spec codes and symbol identifiers match literally rather than approximately, with re-ranking over the candidate set
• Enforce citation-or-decline: calibrate the similarity threshold that triggers a decline, and tune it against a regression set to balance false declines against false answers
• Implement answer-faithfulness verification check each claim for support in the cited chunks before the response is returned
• Extract counts, dimensions and spec values verbatim from the source span rather than paraphrasing, and show the snippet beside any number
• Build revision awareness so the bot answers from the current sheet revision and flags superseded ones, plus conflict and discrepancy detection across sheets
• Treat uploaded documents as untrusted data: instruction-like text inside a plan note must never redirect the bot
• Integrate with the HE2 application so AI-generated counts surface with confidence scores and users can accept, reject or reassign them
• Build the curated Q&A regression suite that scores groundedness and faithfulness on every prompt or model version

What you need

• Strong Python and production experience building RAG systems that are actually in use, not prototypes
• LangChain or equivalent orchestration, and hands-on work with a vector store — Milvus, pgvector, OpenSearch or similar
• Practical grasp of retrieval quality: chunking strategy, hybrid search, re-ranking, and how to measure whether retrieval is the failure or generation is
• Experience with a hosted LLM API; AWS Bedrock and the Claude API specifically is a strong advantage
• FastAPI and async Python service design
• PostgreSQL, and comfort with embedding storage and index lifecycle
• Prompt engineering discipline: evaluation sets, versioned prompts, and regression testing rather than vibes

Nice to have

• Prompt injection and jailbreak mitigation in a production RAG system
• Experience with technical or engineering document corpora
• Cost-aware LLM engineering: prompt caching, batch inference, token budgeting

Building citation and provenance UI alongside the retrieval backend