Senior Forward Deployed Engineer (AI)

Location: On-site in Menlo Park, California, United States

Company

Hippocratic AI is building a healthcare-only, safety-focused LLM platform for AI-native healthcare delivery.

What you will do

• Design and implement advanced RAG systems, tool-calling strategies, MCP integrations, and other LLM engineering patterns.
• Architect and lead complex AI deployments for enterprise healthcare customers.
• Optimize retrieval relevance, latency, cost, and agent behavior across production systems.
• Own reliability through monitoring, logging, incident response, and proactive failure prevention.
• Partner with customer IT teams, Deployment Strategists, and CSEs to solve technical challenges.
• Mentor engineers and contribute to technical standards and best practices.

Requirements

• 5–7 years of software engineering experience with expert-level Python and production systems expertise.
• Advanced hands-on experience with LangChain, LangSmith, RAG, tool calling, and sophisticated LLM patterns.
• Experience architecting complex AI systems for reliability and scale, including enterprise integrations and operational patterns.
• Bachelor’s degree in Computer Science or a related field from a top-ranked university.
• Strong systems design, error handling, monitoring, and production operations experience.

Nice to have

• Experience optimizing advanced RAG systems at scale, including retrieval strategies, embedding models, or vector databases.
• Hands-on experience with MCP or similar tool integration approaches.
• Healthcare IT experience with EHR integrations, FHIR, HL7, or related healthcare data standards.
• Experience mentoring engineers, leading technical initiatives, or contributing to AI/ML open-source projects.
• Track record of driving innovation in AI systems or solving novel technical challenges.

Culture & Benefits

• Work on safety-focused AI systems designed to transform healthcare operations.
• Collaborate with AI engineers and clinical experts from leading technology companies and medical institutions.
• Build systems backed by significant investment from AI, healthcare, and health-system investors.
• Contribute to category-defining AI deployment practices and architectural playbooks.