Lead AI Engineer - Optum

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

We are looking for a Senior GenAI Developer with solid hands-on expertise in both open source and AWS Generative AI stacks. You will design and build production-ready RAG pipelines, agentic AI workflows, and knowledge base chatbots. You will work closely with architects and business stakeholders to deliver accurate, low-hallucination, observable AI solutions at enterprise scale.

You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Primary Responsibilities:

• Design and build production-grade RAG pipelines using LangChain and LangGraph for stateful agentic workflows
• Develop and maintain knowledge base chatbots with semantic search, source attribution, and multi-turn conversation memory
• Implement advanced retrieval strategies - Hybrid Search (vector + keyword), HyDE, and Hierarchical / Small-to-Big chunking
• Build and manage multi-agent orchestration architectures with Agentic RAG patterns (Corrective RAG, Self-RAG)
• Integrate LLMs (Claude, GPT-4o, Llama 3, Mistral) via API and open-source hosting (Fireworks AI, Groq)
• Design and operate vector databases - Pinecone, Weaviate, or PGVector - for semantic knowledge storage
• Build AWS Bedrock-based solutions using Bedrock Agents, Knowledge Bases, and Guardrails
• Set up evaluation frameworks using Ragas (faithfulness, relevance scoring) and maintain Golden Datasets for CI/AI testing
• Implement LLM observability using LangSmith or Phoenix - trace calls, debug hallucinations, monitor costs
• Apply PII redaction pipelines on ingested documents and enforce data security using Model Context Protocol (MCP)
• Deploy AI backends using FastAPI and Docker with OpenTelemetry for system-wide monitoring
• Collaborate with Solution Architects to produce HLDs, LLDs, and developer-facing documentation for AI components

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Required Qualifications: