Associate Director – Product Management, Enterprise AI

Regeneron

. Own the release strategy and roadmap for an assigned portfolio of Regeneron's AI platforms and tools .

Posted 10/27/2026full-timeTarrytown • New York • United StatesSenior💰 $157,200 - $256,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in technical product management for AI platforms, with a strong focus on large language models, generative AI, and MLOps practices. Capable of translating complex business needs into actionable product requirements while ensuring adherence to responsible AI standards.

Highest-signal resume keywords

Technical Product ManagementLarge Language Models (LLMs)MLOps/LLMOps ToolingAI Evaluation and ObservabilityModel Context Protocol (MCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Generative AIPrompt EngineeringModel CustomizationRetrieval-Augmented Generation (RAG)Vector DatabasesData PrivacyAI Governance FrameworksAutomation PipelinesModel Performance ManagementTechnical Scoping

Soft Skills

Analytical RigorStakeholder EngagementProblem-SolvingCommunicationCollaboration

Tools & Technologies

AWSAzureGCPAI PlatformsAnalytics PipelinesAgent-to-Agent (A2A) ProtocolsEmbedding TechniquesQuality Measurement ToolsDrift Monitoring ToolsPrototyping Tools

Industry Keywords

AI/ML CapabilitiesBusiness Case DevelopmentTechnical DeliveryEmbedded EngineeringAutonomous Agents

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud Platform

About the role

Key responsibilities & impact

Own the release strategy and roadmap for an assigned portfolio of Regeneron's AI platforms and tools
Build business cases for developing, acquiring, or investing in AI/ML capabilities, datasets, platforms, and tools
Prioritize competing demands using clear assumptions and analytical rigor
Identify colleague and end-user pain points and high-value experience improvements
Lead technical product development of large-scale AI platforms, machine learning/AI models, and supporting tooling
Define requirements for data access, model serving, search/ranking, automation pipelines, and self-serve tools
Guide the use of AWS, Azure, and GCP for automated ML and analytics pipelines
Run evaluations, pilots, and deployments of new technologies
Drive product adoption by measuring usage, gathering feedback, and continuously improving
Partner with GCC engineering teams to translate architecture decisions and product requirements into production-ready deliverables
Embed with business and scientific teams to scope, build, and deploy AI and agentic solutions from prototype to production
Own discovery, technical scoping, system design, and production rollout for priority deployments
Contribute hands-on to prototypes, integrations, and evaluations
Evaluate and onboard new AI model capabilities, including model upgrades, tool use, and MCP-based integrations
Capture field feedback and feed it into the roadmap and evaluation suites
Codify repeatable patterns into reusable building blocks, playbooks, and reference architectures

Requirements

What you’ll need

Bachelor's degree in related field required
10+ years of progressive experience in technical product management
Strong working knowledge of large language models (LLMs), foundation models, and modern generative AI, including capabilities, limitations, and evaluation
Familiarity with agentic AI, including autonomous and multi-step agents, tool use, planning/orchestration, and human-in-the-loop design
Hands-on understanding of Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols
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
Understanding of AI evaluation and observability, including evals, quality/safety measurement, drift monitoring, and model/agent performance management
Awareness of responsible and secure AI practices, including guardrails, access controls, data privacy, and AI governance frameworks
Familiarity with MLOps/LLMOps tooling and orchestration frameworks
Ability to translate ambiguous business needs into clear product requirements and measurable outcomes
Experience in embedded or customer-facing technical delivery, such as forward deployed engineering, solutions engineering, or applied AI prototyping
Hands-on engineering ability to prototype, integrate, and ship production-ready solutions, including contributing code when needed
Ability to scope and deliver complex deployments in ambiguous, fast-moving environments
Ability to own roadmap, budget inputs, and outcomes for an assigned portfolio

Benefits

Comp & perks

Annual bonuses or other incentive plans
Equity awards
Pension or retirement benefits
401(k) company match
Health and wellness programs
Fitness centers
Medical, dental, vision, life and disability insurance benefits
Paid time off
Family support benefits
Reasonable accommodation during the recruitment process where required