Director, Product Management Applied AI

Job description

We are looking for a product leader to run a portfolio that includes AI-powered products and to own how our Product Management team applies AI — the strategy, standards, investment decisions, governance and team capability. You will lead and develop a team of Product Managers, own product strategy and P&L across the portfolio, and raise the organization's product-AI capability.

What "Applied AI" means at Director level. The emphasis shifts from doing the hands-on AI work to judging it, funding the right AI bets and setting the guardrails. This is a product-leadership role — not an engineering, data-science or AI-research role. You set direction and standards for the specialists who build.

Responsibilities

- Lead and develop a team of Product Managers running a portfolio that includes AI-powered products; own product strategy, vision and P&L across that portfolio

- Set an AI-informed product strategy — where AI creates durable advantage, where it is commoditizing, and how to sequence the roadmap against advancing AI capability

- Make build / buy / partner and investment decisions for AI capabilities; own AI business cases and ROI; fund the right experiments and know when to stop them

- Own AI-feature economics and lifecycle across the portfolio — cost, latency, unit economics, pricing, and model-lifecycle decisions (drift, versioning, vendor change)

- Set the standards, operating model and review gates for how the team builds AI features and uses AI in its own work, so quality and responsibility are consistent

- Own responsible-AI governance — privacy, bias, security, transparency, human oversight, audit trails, incident response, and applicable regulation (e.g., EU AI Act) and sector rules

- Raise the team's AI capability through coaching, playbooks, shared prompt/agent libraries and an AI component in the hiring bar; advise business units or clients on adoption

- Direct effective collaboration across product, engineering, data, design and AI/ML teams, keeping product intent, constraints and accountability clear

- Represent the organization credibly on its AI product strategy with senior clients — setting realistic expectations rather than over-promising

Requirements

- 7+ years in Product Management, having managed products, product lines/families and/or groups, with experience leading PM/PO teams; developed and/or launched 3+ products to market, including AI-powered products or capabilities

- Ownership of product vision, roadmap and P&L, and of strategic roadmap alignment across a portfolio

- Portfolio analysis and strategy formulation; prioritizing spend by ROI and supporting financial models — including AI business cases and build/buy/partner decisions

- Track record aligning product strategy with new technologies, assessing and adopting emerging AI capabilities responsibly

- SME across multiple (3+) business domains; deep grasp of consumer trends, technological disruption and competitive factors — including how AI is reshaping the domain

- AI literacy sufficient to lead — understands AI concepts, capabilities and limitations well enough to make sound portfolio and investment decisions and to challenge technical proposals credibly; ML-engineering / data-science depth is not required

- Able to shape company product strategy, convey difficult messages to senior stakeholders and own key initiatives and business KPIs

- Leads, develops and champions a team of Product Managers across a multi-product portfolio; identifies and plans for performance improvement

- Sets the operating model for how the team applies AI, and raises its proficiency through enablement, coaching and a clear hiring bar

- AI-informed product/portfolio strategy — advantage vs commoditization, build/buy/partner, AI business cases and ROI, roadmap sequencing against AI capability

- Responsible-AI governance across the portfolio — privacy, bias, security, transparency, human oversight, audit trails/incident response and relevant regulation

- Ownership of AI-feature economics and lifecycle — cost, latency, unit economics, pricing, drift/versioning/vendor decisions

- Setting AI standards and review gates for how the team builds AI features and uses AI

- Raising team AI capability — enablement, coaching, playbooks, shared tooling, hiring bar

- Ability to evaluate AI outputs, prototypes and technical proposals and fund the right bets

- Working AI literacy — concepts, capabilities and limitations sufficient to lead and challenge

- Continued practical use of AI in own work and adaptability as AI evolves

- Ability to direct effective collaboration across engineering, data, design and AI/ML teams

Nice to have

- Deep hands-on AI prototyping or feature-building (valued, but expected to plateau here — it is the Senior PM / IC strength; the Director's job is to judge and fund, not build)

- External thought leadership on AI product management (talks, publications, community)

- Direct experience standing up an AI governance or enablement program at organization scale