Mid/Senior Forward Deployed Engineer

Hippocratic AI Inc.; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Hippocratic AI Inc. seeks a Forward Deployed Engineer to own AI deployments that transform health systems by embedding with customers to build, launch, and operate production conversational AI agents.

You will design RAG pipelines grounded in clinical data and connect agents to EHRs and data warehouses while ensuring safety and reliability. You will lead end-to-end deployments, monitor production systems, and partner with product, ML research, and engineering leadership to ship scalable AI in

Qualifications

• LLM techniques: RAG, prompt engineering, tool calling, LLM-as-judge.
• 3+ years of professional software engineering experience.
• Experience building integrations with APIs, databases, or enterprise systems.

Responsibilities

• Own end-to-end AI deployments with health systems.
• Embed with customers to build, launch, and operate production AI agents.
• Design and implement RAG pipelines grounded in customer data.
• Build tool-calling and MCP architectures for secure interactions with customer systems.
• Develop production Python code using LangChain and LangSmith.
• Execute deployments with infrastructure setup, testing, monitoring, and go-live.

Skills

LLM techniques

LangChain/LangSmith

Python

Async patterns & reliability

APIs & enterprise integrations

FHIR/HL7 healthcare data knowledge

Education

Bachelor's degree in Computer Science or related field

Tools

LangChain

LangSmith

Model Context Protocol (MCP)

EHR integrations (Epic, Cerner, Athena)

Job description

Responsibilities

• 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 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
• 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

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field
• 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
• Hands-on experience with LLM frameworks (LangChain, LangSmith, or similar) and deep understanding of modern LLM development patterns and best practices
• 3+ years of professional software engineering experience with strong Python fundamentals and production software development experience
• 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