Full stack AI engineer

- Build and ship the AI engine: retrieval-augmented generation, context-aware reasoning, evidence citation, and the evaluation harness around it.

- Architect production-grade agentic applications using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer.

- Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors.

- Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with ElasticSearch or Solr, BM25 + similarity search, re-ranking.

- Maintain vendor-agnostic LLM abstractions so providers can be swapped behind a clean interface as enterprise constraints evolve.

- Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency.