Vice President, Product - AI Center of Excellence

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Vice President, Product - AI Center of ExcellenceRole 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

o Define and own the multi-year product strategy for the company’s Agent Factory and broader AI platform capabilities.
o Create a product roadmap that supports experimentation, agent build, certification, deployment, monitoring, and commercialization at scale.
o Partner with engineering, architecture, data, security, legal, compliance, risk, and business teams to align AI platform priorities to enterprise strategy.
o Translate emerging AI capabilities, including LLMs, agents, RAG, tool use, memory, evaluation harnesses, and multi-agent orchestration, into practical enterprise product capabilities.
o Prioritize platform investments based on business value, reuse potential, technical feasibility, risk, cost, and customer adoption.

• Agent Factory Control Plane Ownership

o Own 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
o Ensure the control plane provides a consistent, governed path from agent idea to production deployment.
o Partner 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

o Lead the product strategy for AI build capabilities that enable teams to create high-quality agents and LLM-powered applications.
o Support 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
o Establish standard patterns for building agents across data discovery, customer support, engineering productivity, fraud/risk, sales enablement, finance, operations, and internal knowledge workflows.
o Ensure product teams can safely experiment with agents in sandbox environments while maintaining a clear path to certified production deployment.

• Governance, Risk & Compliance

o Embed responsible AI, data governance, privacy, security, and compliance requirements into the product lifecycle.
o Partner with legal, privacy, security, compliance, risk, and audit teams to define practical controls for production AI agents.
o Ensure every production agent has a named owner, risk classification, approved data sources, approved tools, evaluation evidence, telemet…