Lead AI Engineer

Job Title: Lead AI Engineer

Department: Digital Transformation – AI Strategy & Execution

Location: Sleepy Hollow, New York

Employment Type: Full-Time

Experience Required: 7–10 Years

About Regeneron

Regeneron is a leading biotechnology company headquartered in Tarrytown, New York, renowned for its innovative research and development of life-changing therapies. With a strong focus on ophthalmology, oncology, inflammation, cardiovascular diseases, and infectious diseases, Regeneron has brought groundbreaking medicines to market — including EYLEA® and the REGEN-COV™ monoclonal antibody cocktail. Our commitment to scientific excellence, cutting-edge technology, and patient impact has positioned Regeneron as a global leader in biotechnology.

Position Overview

Regeneron is seeking an experienced Lead AI Engineer to serve as a technical leader within the AI Strategy & Execution organization. In this role, you will architect and deliver enterprise-scale AI capabilities — spanning intelligent agents, LLM-powered applications, and AI platform services — that accelerate drug discovery, clinical research, and enterprise productivity. You will lead a team of engineers, drive cross-functional collaboration, and communicate AI strategy to senior stakeholders, all while maintaining a strong hands-on presence in Python development and AI systems design.

Key Responsibilities

AI Engineering & Intelligent Systems

• Lead end-to-end design, development, and deployment of enterprise AI applications and LLM-powered systems, ensuring production-grade quality, security, and scalability.

• Design and build AI Agents and autonomous systems using agentic frameworks including LangChain, LangGraph, Strands, and CrewAI, applying ReAct, tool-use, planning, and memory management patterns.

• Develop and operate MCP (Model Context Protocol) servers for standardized, secure integration between AI agents and enterprise data sources.

• Apply advanced LLM techniques — prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) — to build intelligent, context-aware applications.

• Architect and manage vector database solutions (Milvus DB preferred) supporting semantic search, knowledge retrieval, and RAG pipelines.

• Architect RESTful APIs that expose AI capabilities to internal platforms and downstream business applications.

LLM & AI Platform Management

• Administer and continuously evolve the enterprise LLM and AI Gateway platform, including model routing, access controls, rate limiting, usage observability, and multi-model orchestration.

• Evaluate and onboard new foundation models (OpenAI, Anthropic, AWS Bedrock, and others) in alignment with enterprise security and compliance standards.

Technical Leadership & Team Management

• Lead, mentor, and manage a team of AI engineers, conducting code reviews, setting technical standards, and fostering a culture of engineering excellence.

• Contribute to the organization’s AI roadmap through architectural design reviews, technical strategy, and proof-of-concept development.

• Troubleshoot complex issues across AI model serving, APIs, and platform layers.

Cross-Functional Collaboration & Stakeholder Engagement

• Partner with business units, data scientists, and clinical research teams to identify AI opportunities and translate them into scalable technical solutions.

• Serve as the primary technical point of contact for enterprise AI platform discussions, presenting findings and recommendations to senior leadership.

• Engage with external vendors, cloud providers, and AI research organizations to evaluate and integrate emerging capabilities.

Requirements & Qualifications

Education

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence or a related technical field.

Experience

• 7–10 years of progressive software and AI engineering experience, with at least 3 years in a technical lead or senior individual contributor role.

• Expert-level Python development skills, including production REST API development with FastAPI.

• Proven hands-on experience building AI Agents, MCP Servers, and LLM-powered applications using frameworks such as LangChain, LangGraph, Strands, or CrewAI.

• Hands-on experience with vector databases (Milvus DB preferred) for RAG and semantic search pipelines.

• Experience administering LLM API gateway plat…