Lead AI Engineer

Regeneron Pharmaceuticals•Sleepy Hollow, WY

8d•$109,900 - $179,300•Onsite

About The Position

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.

Requirements

Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence or a related technical field.
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 platforms, including model routing, observability, access governance, and multi-model orchestration.
Experience with foundation models from OpenAI, Anthropic, AWS Bedrock, and other major providers.
Working knowledge of AWS cloud services relevant to AI workloads (SageMaker, Bedrock, EKS, Lambda, S3); Kubernetes and CI/CD experience is a plus.
Experience in the biotechnology, pharmaceutical, or healthcare industry preferred.
Programming: Python (expert-level).
AI Frameworks & Runtimes: LangChain, LangGraph, Strands, CrewAI; AWS Bedrock AgentCore.
LLM Techniques: Prompt engineering, fine-tuning, RAG, vector databases (Milvus DB preferred).
AI Platform: LLM Gateway management, model routing, multi-model orchestration, TensorFlow, PyTorch.
API Development: RESTful API design, FastAPI, OpenAPI/Swagger.
Cloud & Infrastructure (supporting): AWS (SageMaker, Bedrock, EKS, Lambda, S3), Kubernetes, Docker, Terraform, CI/CD pipelines.
Data & Analytics: Databricks, Dataiku, Postgres, Redis, NoSQL databases.
Observability: Monitoring, logging, and distributed tracing for AI workloads.
AWS Certified Machine Learning – Specialty

Nice To Haves

AWS Certified Solutions Architect – Professional
Certified Kubernetes Administrator (CKA)
Exceptional communication skills with the ability to present AI concepts to both engineering teams and executive stakeholders.
Proven track record of leading engineering teams and managing delivery across cross-functional groups.
Strong problem-solving mindset with a bias toward pragmatic, production-ready AI solutions.
Collaborative and proactive in engaging with business units, compliance, and external partners.

Responsibilities

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.
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.
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.
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.