• Production RAG: indexing, retrieval, hybrid search, reranking, query rewriting, grounding, citations • Context Graph: entity resolution + linking + provenance; graph + vector retrieval; supports multi-hop context • LLM orchestration: tool/function calling, structured outputs, routing across model tiers, failure modes • GPU/inference cost optimization: batching, caching/KV reuse, quantization, autoscaling; optimize $/session + latency • Safety + compliance: PII/PHI handling, redaction, audit logs, deterministic replay, hallucination mitigation • LLMOps: eval harness (golden sets, regression, adversarial), monitoring for quality/cost/drift • Design/ship the end-to-end pipeline: retrieve → assemble context → generate → cite → log/monitor • Improve quality and trust via evaluation, feedback loops, and clear evidence-backed outputs • Partner with product, security, and domain teams; write crisp design docs; raise engineering bar • Ship RAG v1 with citations + measurable quality metrics • Deliver Context Graph v1 that improves retrieval on real copilot tasks • Reduce cost/latency with a concrete inference optimization plan shipped to prod.