Director, Product Management – Enterprise AI Platforms & Tools

Role Overview

• Be responsible for the release strategy and roadmap for Regeneron's AI platforms and tools, aligned to the enterprise digital roadmap.
• Build business cases to develop, acquire, or invest in new AI/ML capabilities, datasets, platforms, and tools.
• Prioritize ruthlessly across competing demands, framing recommendations with clear assumptions and analytical rigor.
• Develop deep insight into colleague and end-user pain points and identify the highest-value opportunities to improve their experience.
• Lead the technical product development of large-scale AI platforms, machine learning/AI models, and supporting tooling, partnering with engineering and Business Digital teams to ship high-quality deliverables.
• Define requirements for core platform capabilities — data access, model serving, search/ranking, automation pipelines, and self-serve tools.
• Guide the use of cloud technologies (AWS, Azure, GCP) for building and deploying automated ML and analytics pipelines.
• Setting the evaluation framework and criteria, pilot, and deploy new technologies as appropriate, building toward an industry-leading internal AI ecosystem.
• Drive product adoption: measure usage, gather feedback, and continuously improve.
• Partner with GCC engineering teams to translate platform architecture decisions and product requirements into well-scoped, production-ready deliverables; maintain ongoing alignment on roadmap priorities, handoff standards, and delivery quality to ensure continuity between applied AI prototyping and GCC-led build and operate.

Requirements

• A Bachelor's degree in related field required, Masters, MBA, or advanced degree preferred
• 12+ years of progressive experience in technical product management
• Strong working knowledge of large language models (LLMs), foundation models, and modern generative AI — including their capabilities, limitations, and evaluation.
• Familiarity with agentic AI: autonomous and multi-step agents, tool use, planning/orchestration, and human-in-the-loop design.
• Hands-on understanding of emerging interoperability standards, including the Model Context Protocol (MCP) for connecting models to tools and data, and Agent-to-Agent (A2A) protocols for multi-agent coordination.
• Experience with retrieval-augmented generation (RAG), vector databases, embeddings, and grounding models in enterprise data.
• Familiarity with prompt engineering, context engineering, fine-tuning, and model customization techniques.
• Understanding of AI evaluation and observability — building evals, measuring quality/safety, monitoring drift, and managing model/agent performance in production.
• Awareness of responsible and secure AI practices: guardrails, access controls, data privacy, and AI governance frameworks.
• Familiarity with MLOps/LLMOps tooling and orchestration frameworks for building, deploying, and maintaining AI applications and pipelines.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform

Benefits

• annual bonuses or other incentive plans
• equity awards
• pension or retirement benefits
• 401(k) company match
• health and wellness programs
• fitness centers
• insurance benefits (e.g. medical, dental, vision, life and disability)
• paid time off
• family support benefits