Sr. Director, Agentic Product Management

NetApp, Inc.•Durham, NC

1d•$266,050 - $396,000

About The Position

This role leads the adoption of AI-native workflows and agentic systems across product management, design, engineering, and the operating rhythm that surrounds them, translating enterprise AI strategy into the programs, capabilities, and practices that product and engineering teams use day to day. Change management is the central craft of the role: leading adoption motions inside the CPO organization and partnering across GTM, security, and compliance to ensure AI-native workflows land cleanly at every touchpoint. Through AI adoption, the role delivers 2-3× productivity gains across the lifecycle and 33-50% lift in engineering velocity, product throughput, and revenue efficiency. This work spans AI-assisted product discovery and prioritization, design and specification, code generation and review, test and documentation automation, and NPI readiness. This leader will drive and define the lifecycle-scoped governance and cost-control practices that make adoption defensible: acceptable use, model approval, cost metering and chargeback, and the risk posture for agentic systems.

Requirements

10+ years of strategic and people leadership experience across product management, digital transformation, or engineering-adjacent roles, with demonstrated ownership of end-to-end transformation programs from strategy through adoption and measurable impact.
Proven success leading and implementing strategic AI or digital adoption at scale, including redesign of operating models, workflows, and governance—not just tool deployment. Experience may come from top-tier consulting practices, internal AI or platform teams at scaled technology companies, or senior operating roles at AI‑first startups.
Hands-on fluency with modern AI and agent tooling, including LLMs, coding assistants, and agent or orchestration frameworks, with the ability to make credible technical and architectural judgments.
Strong change management and stakeholder leadership skills, with experience partnering across product, engineering, GTM, security, legal, and compliance to land AI-native workflows responsibly.
Demonstrated ability to operate in high-ambiguity environments, define problems and success criteria, and drive alignment among senior leaders as evidence and direction evolve; strong experience leading change management initiatives.
Exceptional leadership and people management skills, with the ability to inspire and lead high-performing, cross-functional teams and drive accountability.

Responsibilities

Define and lead the framework, success criteria, operating plan, and roadmap for the agentic AI and AI-native capabilities that the CPO organization will build and adopt - including prioritization, sequencing, and translation of enterprise AI strategy into executable workstreams across product discovery, design, engineering, operations, and NPI.
Lead a team to reimagine the product and engineering operating system with AI - applying AI-native decision support to roadmap prioritization, budget and resource allocation, portfolio management, and the rhythm of the business.
Drive AI adoption and change management inside the CPO organization - enablement, ways-of-working evolution, and the cross-functional partnership with GTM, security, and compliance.
Own the AI-driven workflow strategy across the product development lifecycle, defining how AI assistants and agents are introduced, scaled, and retired across PM, design, engineering, and operations teams.
Provide vision and direction to direct team and cross-functional stakeholders, translating strategy into multi-functional objectives.
Lead the AI agent program across the lifecycle - defining success metrics and criteria for agents spanning discovery, design, code review, test, documentation, planning, and NPI.
Identify, prototype, and launch high-impact AI use cases that demonstrate measurable productivity and financial impact across the lifecycle.
Define the platform strategy for internal AI capabilities so product teams consume shared, paved-road infrastructure rather than building bespoke AI plumbing.
Establish metering, chargeback, and optimization standards that keep AI costs sustainable across product and engineering workloads.
Shape the longer-horizon operating model for an agent-augmented product organization as autonomous agents take on a greater scope of work.