Forward Deployed Engineer

Core Responsibilities
AI Innovation & Problem-Solving – Identify customer challenges and design AI solutions using advanced LLM techniques (RAG, tool calling, LLM-as-judge); implement novel approaches to healthcare AI problems

RAG Pipeline Implementation – Design and implement retrieval-augmented generation pipelines that ground LLM responses in customer data, ensuring accuracy and clinical relevance

Tool & MCP Architecture – Implement tool-calling architectures and Model Context Protocol (MCP) connections that enable agents to interact with customer systems safely

Python AI Development – Build production Python code using LangChain, LangSmith, and modern AI frameworks to create reliable AI systems

Infrastructure & Deployment – Set up secure, monitored production environments; manage deployment and go-live activities

Production Monitoring & Support – Monitor deployed systems, respond to incidents, troubleshoot problems with customers, implement fixes

What You BringMust Haves
3-5 years of software engineering experience with strong Python fundamentals and production software development experience

Hands-on experience with LLM frameworks (LangChain, LangSmith) and understanding of modern LLM development patterns

Deep understanding of LLM techniques: RAG, prompt engineering, tool calling, LLM-as-judge, and related patterns

Experience building integrations with APIs, databases, or enterprise systems; comfort with async patterns and error handling

Bachelor's degree in Computer Science (or related field) from a top ranked university.

Nice to Haves
Experience with Model Context Protocol (MCP) or similar approaches for tool integration

Healthcare IT experience (EHR integrations, FHIR, HL7, healthcare data standards)

Production DevOps or infrastructure experience; ability to set up monitoring and incident response

Experience deploying AI systems or working with LLMs in production environments

Why Hippocratic AI
You're innovating in AI-native healthcare. You're not building traditional integrations—you're solving cutting-edge problems in conversational AI for healthcare. Every RAG pipeline you design, every tool-calling pattern you implement, every LLM optimization you discover contributes to healthcare transformation.

You work with world-class AI engineers and clinical experts from Google, Meta, Microsoft, NVIDIA, Johns Hopkins, Washington University, Stanford. You'll tackle harder AI problems alongside smarter people. You'll grow your AI expertise faster here than almost anywhere.

Backed by $404M from top-tier AI and healthcare investors—CapitalG, a16z, General Catalyst, Kleiner Perkins, plus strategic health system investors. This validates the category and gives you the resources to focus on innovation without runway concerns.

This is category creation. You're defining how LLM agents interact with healthcare systems, how RAG grounds clinical AI, how to deploy AI safely at scale. You're not following playbooks—you're writing them..

Skills Required
3-5 years software engineering experience with strong Python fundamentals and production software development experience
Hands-on experience with LLM frameworks (LangChain, LangSmith)
Deep understanding of LLM techniques: RAG, prompt engineering, tool calling, LLM-as-judge
Experience building integrations with APIs, databases, or enterprise systems; comfort with async patterns and error handling
Bachelor's degree in Computer Science or related field from a top ranked university
Experience with Model Context Protocol (MCP) or similar approaches for tool integration
Healthcare IT experience (EHR integrations, FHIR, HL7, healthcare data standards)
Production DevOps or infrastructure experience; ability to set up monitoring and incident response
Experience deploying AI systems or working with LLMs in production environments