Title and Summary

Vice President, Product - AI Center of Excellence

Role Overview
We are seeking a Vice President, AI Product Management to lead product strategy and execution for the company's enterprise AI platform capabilities, including the Agent Factory control plane, agent development lifecycle, LLM enablement, evaluation frameworks, and governed AI build patterns.
This leader will own the product vision, roadmap, and operating model for enabling internal teams to design, build, evaluate, deploy, monitor, and scale trusted AI agents across the enterprise. The role will manage a small team of Product Managers and Product Managers-Technical responsible for the platform capabilities that make AI agent development safe, reusable, observable, and production-ready.

The ideal candidate has experience building enterprise platforms, developer platforms, AI/ML products, data platforms, or cloud-native infrastructure products in a regulated or highly governed environment. They should be comfortable operating at the intersection of product strategy, technical architecture, data governance, risk management, and commercial value creation.

Key Responsibilities:
-Product Strategy & Roadmap
oDefine and own the multi-year product strategy for the company's Agent Factory and broader AI platform capabilities.
oCreate a product roadmap that supports experimentation, agent build, certification, deployment, monitoring, and commercialization at scale.
oPartner with engineering, architecture, data, security, legal, compliance, risk, and business teams to align AI platform priorities to enterprise strategy.
oTranslate emerging AI capabilities, including LLMs, agents, RAG, tool use, memory, evaluation harnesses, and multi-agent orchestration, into practical enterprise product capabilities.
oPrioritize platform investments based on business value, reuse potential, technical feasibility, risk, cost, and customer adoption.

-Agent Factory Control Plane Ownership
oOwn the product direction for the Agent Factory control plane, including:
• Agent registry
• Agent lifecycle management
• Intake and approval workflows
• Risk tiering
• Model gateway
• Prompt registry
• Tool registry
• Evaluation and certification workflows
• Agent deployment governance
• Observability and audit evidence
• FinOps and value measurement
oEnsure the control plane provides a consistent, governed path from agent idea to production deployment.
oPartner with engineering to ensure the control plane integrates with AWS, Databricks, PCF/on-prem environments, enterprise identity, access controls, data governance, and observability systems.

-AI Build & Agent Enablement
oLead the product strategy for AI build capabilities that enable teams to create high-quality agents and LLM-powered applications.
oSupport platform capabilities such as:
• Agent Studio / builder experience
• Prompt playgrounds
• Agent templates
• LLM model access
• RAG patterns
• Tool-calling frameworks
• Agent evaluation harnesses
• Guardrails
• Human-in-the-loop workflows
• Runtime deployment patterns
• Reusable agent components
oEstablish standard patterns for building agents across data discovery, customer support, engineering productivity, fraud/risk, sales enablement, finance, operations, and internal knowledge workflows.
oEnsure product teams can safely experiment with agents in sandbox environments while maintaining a clear path to certified production deployment.

-Governance, Risk & Compliance
oEmbed responsible AI, data governance, privacy, security, and compliance requirements into the product lifecycle.
oPartner with legal, privacy, security, compliance, risk, and audit teams to define practical controls for production AI agents.
oEnsure every production agent has a named owner, risk classification, approved data sources, approved tools, evaluation evidence, telemetry, and a support model.
oDefine approval gates and production-readiness criteria based on agent autonomy, data sensitivity, tool access, regulatory exposure, and business impact.
oDrive consistency across AI governance, model governance, data governance, and enterprise access-control policies.

-Customer Adoption & Commercialization
oDrive adoption of the Agent Factory across internal product, engineering, data, and business teams.
oCreate product experiences, onboarding guides, documentation, templates, and enablement programs that make it easier for teams to build agents through the platform.
oDevelop metrics to track usage, adoption, reuse, quality, cost, risk, and business value.
oSupport the evolution of the Agent Factory from internal platform capability to commercializable AI product foundation.
oPartner with business and product teams to identify AI agent capabilities that can be embedded into customer-facing products, partner solutions, or monetizable services.

Required Qualifications
-Experience owning enterprise-scale platforms, internal developer platforms, AI/ML platforms, data platforms, cloud platforms, workflow platforms, or governed technology products.
-Strong understanding of generative AI, LLMs, AI agents, RAG, prompt management, model evaluation, tool use, and enterprise AI risk considerations.
-Experience working in regulated, security-conscious, or highly governed environments such as financial services, payments, banking, insurance, healthcare, or large enterprise technology.
-Ability to translate complex technical capabilities into clear product strategy, roadmaps, executive narratives, and customer-facing value propositions.
-Strong executive communication skills with the ability to influence senior leaders across product, engineering, data, security, risk, compliance, legal, and business functions.
-Experience defining product metrics, OKRs, adoption goals, platform KPIs, and business value measures.
-Strong technical fluency with cloud, APIs, platform architecture, data governance, identity/access management, observability, and CI/CD concepts.

Pay Ranges
New York City, New York: $245,000 - $391,000 USD