Lead AI Engineer

Lead AI Engineer

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.

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 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 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.

Technical Competencies

• 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.

Certifications

Required:

• AWS Certified Machine Learning – Specialty

Nice to Have:

• AWS Certified Solutions Architect – Professional
• Certified Kubernetes Administrator (CKA)

Soft Skills & Leadership

• 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.