Hippocratic AI - Forward Deployed Engineer (Mid/Senior) - Remote w/Travel

• 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. • Own your first major outcome: • 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 rely on to solve their hardest AI problems, and generate measurable evidence that our agents improve operational reliability and clinical outcomes—validating our technology in production healthcare environments. • Drive lasting impact: • The Team • The Team • You'll work alongside Deployment Strategists, engineers, and clinical experts—embedded with customers but tightly connected to our core AI team. You'll operate with high technical ownership and autonomy in the field, with direct access to our product, ML research, and engineering leadership. This is a culture of shipping real systems, owning outcomes, and solving problems before they become crises. • 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 • Design and implement RAG pipelines • 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 • Build tool-calling and Model Context Protocol (MCP) architectures • Develop production Python code using LangChain, LangSmith, and modern AI frameworks to implement advanced LLM techniques (RAG, prompt engineering, LLM-as-judge, chain-of-thought reasoning) solving novel healthcare AI problems • Develop production Python code • Execute end-to-end deployments including infrastructure setup, integration testing, production monitoring configuration, cutover planning, and go-live execution—ensuring deployments happen on schedule without surprises • Execute end-to-end deployments • Monitor and own production systems by instrumenting deployed agents, responding quickly to incidents, troubleshooting issues collaboratively with customers, and implementing fixes that keep systems running reliably • Monitor and own production systems • Partner with customers as technical expert, explaining AI system architecture, helping teams understand capabilities and limitations, and building confidence in the solution through proactive communication and problem-solving • Partner with customers as technical expert