Director, Product Management | Applied AI

EPAM Systems•New York, NY

About The Position

The Director of Product Management leads a portfolio that includes AI-powered products and owns how the Product Management team applies AI, including strategy, standards, investment decisions, governance, and team capability. This is a product-leadership role focused on judging AI opportunities, funding appropriate bets, and setting guardrails—not on engineering, data science, or AI research. The Director sets direction and standards for the specialists who build AI capabilities. The role leads and develops Product Managers, owns product strategy, vision, and P&L across the portfolio, and raises the organization’s product-AI capability. It sets AI-informed product strategy, makes build/buy/partner and investment decisions, owns AI business cases and ROI, and guides AI-feature economics and lifecycle management. The Director also establishes responsible-AI governance and team operating standards, improves AI proficiency through coaching and enablement, coordinates across product and technical disciplines, and represents the organization’s AI product strategy to senior clients.

Requirements

7+ years of Product Management experience, including managing products, product lines/families, and/or groups, and experience leading PM/PO teams.
Developed and/or launched 3+ products to market, including AI-powered products or capabilities.
Experience owning product vision, roadmap, and P&L, and aligning strategic roadmaps across a portfolio.
Experience with portfolio analysis and strategy formulation, prioritizing spend by ROI, and supporting financial models, including AI business cases and build/buy/partner decisions.
Track record of aligning product strategy with new technologies and responsibly assessing and adopting emerging AI capabilities.
Subject-matter expertise across 3+ business domains and a deep understanding of consumer trends, technological disruption, competitive factors, and how AI is reshaping those domains.
AI literacy sufficient to understand AI concepts, capabilities, and limitations; make sound portfolio and investment decisions; and credibly challenge technical proposals. ML-engineering and data-science depth are not required.
Ability to shape company product strategy, communicate difficult messages to senior stakeholders, and own key initiatives and business KPIs.
Ability to lead, develop, and champion Product Managers across a multi-product portfolio, including identifying and planning for performance improvement.
Ability to set the team’s AI operating model and raise its proficiency through enablement, coaching, and a clear hiring bar.
Knowledge of AI-informed product and portfolio strategy, including advantage versus commoditization, build/buy/partner decisions, AI business cases and ROI, and roadmap sequencing against AI capability.
Ability to own responsible-AI governance across a portfolio, including privacy, bias, security, transparency, human oversight, audit trails, incident response, and relevant regulation.
Ability to own AI-feature economics and lifecycle, including cost, latency, unit economics, pricing, drift, versioning, and vendor decisions.
Ability to set AI standards and review gates for building AI features and using AI within the team.
Ability to raise team AI capability through enablement, coaching, playbooks, shared tooling, and hiring standards.
Working AI literacy sufficient to lead and challenge, along with continued practical use of AI and adaptability as AI evolves.
Ability to direct effective collaboration across engineering, data, design, and AI/ML teams.

Nice To Haves

Deep hands-on AI prototyping or feature-building experience.
External thought leadership on AI product management, such as talks, publications, or community involvement.
Direct experience establishing an AI governance or enablement program at organizational scale.

Responsibilities

Lead and develop a team of Product Managers managing a portfolio that includes AI-powered products; own product strategy, vision, and P&L across the portfolio.
Set an AI-informed product strategy, identifying where AI creates durable advantage versus commoditizes and sequencing the roadmap as AI capabilities advance.
Make build, buy, partner, and investment decisions for AI capabilities; own AI business cases and ROI, fund appropriate experiments, and determine when to stop them.
Own AI-feature economics and lifecycle across the portfolio, including cost, latency, unit economics, pricing, and model lifecycle decisions such as drift, versioning, and vendor changes.
Set standards, the operating model, and review gates for building AI features and using AI in the team’s work.
Own responsible-AI governance, including privacy, bias, security, transparency, human oversight, audit trails, incident response, applicable regulation, and sector rules.
Raise the team’s AI capability through coaching, playbooks, shared prompt and agent libraries, enablement, and an AI component in the hiring bar.
Advise business units or clients on AI adoption.
Direct effective collaboration across product, engineering, data, design, and AI/ML teams, keeping product intent, constraints, and accountability clear.
Represent the organization credibly to senior clients on AI product strategy and set realistic expectations.
Shape company product strategy, communicate difficult messages to senior stakeholders, and own key initiatives and business KPIs.
Identify and plan for performance improvement across the Product Manager team.
Evaluate AI outputs, prototypes, and technical proposals and fund appropriate opportunities.
Continue practical use of AI in the Director’s own work and adapt as AI evolves.