Hippocratic AI builds the safest generative AI healthcare agent for health systems, payors, and pharma. Over 180 million clinical interactions across 1,000+ use cases with 60+ partners worldwide. Backed by General Catalyst, Kleiner Perkins and a16z.
About the role
Design and implement RAG pipelines that ground conversational AI responses in customer clinical data, ensuring accuracy, safety, and relevance to healthcare workflows while managing retrieval latency and data governance Build tool-calling and Model Context Protocol (MCP) architectures that enable AI agents to interact securely with customer systems—EHRs (Epic, Cerner, Athena), data warehouses, and operational tools—handling errors gracefully and enforcing safety constraints
What they're looking for
- Bachelor's degree in Computer Science, Software Engineering, or a related technical field
- 3+ years of professional software engineering experience with strong Python fundamentals and production software development experience
- Hands-on experience with LLM frameworks (LangChain, LangSmith, or similar) and deep understanding of modern LLM development patterns and best practices
- Deep expertise in LLM techniques including retrieval-augmented generation (RAG), prompt engineering, tool calling, LLM-as-judge, and related advanced patterns
- Demonstrated experience building integrations with APIs, databases, or enterprise systems, comfort with async patterns, error handling, and reliability engineering
- Experience with Model Context Protocol (MCP) or similar frameworks for tool integration and multi-system orchestration
More about this role
As HAI's Forward Deployed Engineer, you will be the technical owner of AI deployments that directly transform how health systems operate. You'll embed with customers to build, launch, and operate production conversational AI agents—architecting systems that handle real clinical workflows and impact thousands of patient interactions. This role exists because healthcare organizations are ready to deploy breakthrough AI at scale, and they need a world-class AI engineer who can build reliable, innovative systems in the field.
Own your first major outcome: By day 90, you will have completed end-to-end ownership of your first AI deployment: designed and implemented a RAG pipeline grounded in customer data, built tool-calling and MCP integrations connecting our agents to customer systems (EHRs, data warehouses, operational tools), executed a production go-live with zero surprises, and established monitoring that catches anomalies before customers do.
Drive lasting impact: At 12 months, you will have deployed multiple agents across your assigned health system, built reusable AI patterns and frameworks that accelerate future deployments, become the trusted technical partner that customers...
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