Senior RAG - Document AI Engineer

Senior RAG - Document AI Engineer

Design, build and operate the Python/FastAPI microservices that turn heterogeneous enterprise documents into accurate, cited answers from document parsing and chunking through enrichment, hybrid retrieval and evaluation. You own retrieval quality and the evidence for it.

Responsibilities

• Build document processing services: format-aware parsing across native text, OCR and vision-language models; table and figure extraction.
• Build a configurable chunking service with strategies selectable per document class, maintained as versioned pipeline profiles.
• Implement LLM-based metadata extraction into strict JSON schemas, with confidence scoring and human-in-the-loop review.
• Build the retrieval service: query rewriting, hybrid dense + BM25 with rank fusion, metadata pre-filtering, cross-encoder reranking, context assembly with citations.
• Own the evaluation harness — golden Q&A sets, recall@k, nDCG/MRR, groundedness — and gate releases on measured quality.
• Instrument, monitor and support the services in production.

Qualifications

• 6–10 years software engineering, with 2+ years building retrieval or document-AI systems used by real users in production.
• Production RAG at scale. 100k+ documents and millions of chunks; p95 query latency under 2s, sustained under concurrent load.
• Operational maturity. Incremental and delta ingest; has re-indexed a live corpus without downtime after a chunking or embedding-model change.
• Chunking as a measured design decision. Hierarchical parent–child and section-aware strategies — not a single global token size.
• Retrieval depth. Hybrid dense + sparse (BM25) retrieval and rank fusion; cross-encoder reranking; embedding model selection and evaluation.
• Retrieval evaluation in practice. Recall@k, nDCG/MRR and a groundedness measure, used to justify changes.
• Document processing. Messy real-world PDF, PPTX and DOCX; OCR pipelines and their failure modes; vision-language models for charts and infographics.
• Python and FastAPI in production. Python 3.11+, async, Pydantic, streaming/SSE, OpenAPI contracts, API versioning.
• Service engineering. Queue and worker patterns, idempotency, retries, dead-letter handling; Docker, pytest, Git and CI; structured logging and tracing.
• AWS as a consumer. S3, ECS/EKS, Lambda, Bedrock, Textract, OpenSearch; a vector database in production with collection design and metadata filtering.

Preferred Qualifications

• Life sciences, pharma or other regulated content; PII/PHI handling.
• Multimodal retrieval; MCP or agent tool surfaces.
• Judgement on frameworks — has used LangChain or LlamaIndex and can say when not to.