Director Product Management — Enterprise AI Platforms & Tools

Regeneron Pharmaceuticals•Tarrytown, GA

2d•$183,100 - $305,200•Onsite

About The Position

Regeneron’s newly established Enterprise Data & AI organization is built to drive AI transformation and adoption at scale across every part of the organization. This is a high-visibility, high-velocity position where the work is directly tied to business and scientific impact. As the product leader for Regeneron's AI platforms and tools, the successful candidate will have the potential for massive impact and help shape how the enterprise builds, accesses, and adopts AI capabilities. The Director Product Management – Enterprise AI Platforms & Tools will set AI platform strategy and roadmap for the platforms and tools that put AI into the hands of colleagues across the organization, from internal AI assistants to secure data-connected applications to agentic solutions. They will translate business needs into clear technical requirements, frame decisions with a transparent view into their assumptions and the rigor behind them and proactively use data to inspire the next generation of capabilities. The successful candidate will be part of the Enterprise Data & AI (ED&AI) team within the AI Center of Excellence (AICOE), working closely with the Chief AI Officer, the AI Innovation Leader, the Platform Engineering team, Delivery Managers, and BU IT partners. Together with this network, they will leverage Regeneron's rich data and state-of-the-art infrastructure to deliver platforms that are useful, trusted, and adopted at enterprise scale.

Requirements

A Bachelor's degree in related field required
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.

Nice To Haves

Masters, MBA, or advanced degree preferred

Responsibilities

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.