Mid/Senior Forward Deployment Engineer

Who you are

• 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
• Healthcare IT experience, including EHR integrations (Epic, Cerner, Athena), FHIR, HL7, or healthcare data standards
• Production DevOps or infrastructure experience, including setting up monitoring, alerting, logging, and incident response systems
• Track record deploying AI systems or working with LLMs in production environments, managing latency, reliability, and operational complexity
• Experience in mission-critical or safety-sensitive systems where reliability and error handling are non-negotiable
• Startup or high-growth technology background, particularly in technical leadership or ownership roles

What the job involves

• 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
• 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
• 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
• 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
• 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
• 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