Director Product Management — Enterprise AI Platforms & Tools
Regeneron
August 20, 2026
On-site
Tarrytown, NY
IT
Responsibilities:
- Own release strategy and roadmap for enterprise AI platforms and tools, aligned to the enterprise digital roadmap.
- Build business cases to develop, acquire, or invest in AI/ML capabilities, datasets, platforms, and tools.
- Prioritize across competing demands with clear assumptions and analytical rigor.
- Identify colleague/end-user pain points and highest-value opportunities to improve experience.
- Lead technical product development of large-scale AI platforms, ML/AI models, and supporting tooling; partner to ship high-quality deliverables.
- Define requirements for core platform capabilities: data access, model serving, search/ranking, automation pipelines, and self-serve tools.
- Guide use of AWS, Azure, and/or GCP to build and deploy automated ML/analytics pipelines.
- Establish evaluation framework and criteria; pilot and deploy new technologies; build an internal AI ecosystem.
- Drive adoption: measure usage, gather feedback, and continuously improve.
- Partner with GCC engineering teams to translate architecture/product requirements into production-ready deliverables and maintain roadmap/handoff/delivery quality alignment.
Qualifications/Skills:
- Bachelor’s degree required (Master’s/MBA/advanced degree preferred).
- 12+ years progressive technical product management experience.
- Strong knowledge of LLMs/foundation models/generative AI (capabilities, limitations, evaluation).
- Familiarity with agentic AI (autonomous/multi-step agents, tool use, planning/orchestration, human-in-the-loop).
- Hands-on understanding of interoperability standards: MCP and A2A.
- Experience with RAG, vector databases, embeddings, and grounding models in enterprise data.
- Familiarity with prompt/context engineering, fine-tuning, and model customization.
- Understanding of AI evaluation and observability (e.g., evals, quality/safety, drift, performance in production).
- Awareness of responsible/secure AI (guardrails, access controls, data privacy, AI governance).
- Familiarity with MLOps/LLMOps tooling and orchestration frameworks.
Application instructions:
- Apply now.
- Own release strategy and roadmap for enterprise AI platforms and tools, aligned to the enterprise digital roadmap.
- Build business cases to develop, acquire, or invest in AI/ML capabilities, datasets, platforms, and tools.
- Prioritize across competing demands with clear assumptions and analytical rigor.
- Identify colleague/end-user pain points and highest-value opportunities to improve experience.
- Lead technical product development of large-scale AI platforms, ML/AI models, and supporting tooling; partner to ship high-quality deliverables.
- Define requirements for core platform capabilities: data access, model serving, search/ranking, automation pipelines, and self-serve tools.
- Guide use of AWS, Azure, and/or GCP to build and deploy automated ML/analytics pipelines.
- Establish evaluation framework and criteria; pilot and deploy new technologies; build an internal AI ecosystem.
- Drive adoption: measure usage, gather feedback, and continuously improve.
- Partner with GCC engineering teams to translate architecture/product requirements into production-ready deliverables and maintain roadmap/handoff/delivery quality alignment.
Qualifications/Skills:
- Bachelor’s degree required (Master’s/MBA/advanced degree preferred).
- 12+ years progressive technical product management experience.
- Strong knowledge of LLMs/foundation models/generative AI (capabilities, limitations, evaluation).
- Familiarity with agentic AI (autonomous/multi-step agents, tool use, planning/orchestration, human-in-the-loop).
- Hands-on understanding of interoperability standards: MCP and A2A.
- Experience with RAG, vector databases, embeddings, and grounding models in enterprise data.
- Familiarity with prompt/context engineering, fine-tuning, and model customization.
- Understanding of AI evaluation and observability (e.g., evals, quality/safety, drift, performance in production).
- Awareness of responsible/secure AI (guardrails, access controls, data privacy, AI governance).
- Familiarity with MLOps/LLMOps tooling and orchestration frameworks.
Application instructions:
- Apply now.