Product Manager, Agentic Operations

NetApp, Inc.•Durham, NC

1d•$196,350 - $292,600

About The Position

This role partners with leaders across NetApp to apply AI-native ways of working to high-value processes inside the company. The work is hands-on and engagement-based: partner with a team to understand how their work runs today, design a future state where AI agents accelerate and augment the work, build and pilot the solution, and partner with the team to make the new way of working durable. The role applies product management discipline — diagnosis, prioritization, specification, iteration, and measurement — to the design and delivery of agent-driven workflows. The unit of delivery is a working solution adopted by a partner team, not a roadmap or a recommendation. The role sits within the Agentic Product Management team under the VP, Analytics Growth and AI Native Engineering, and reports to the Director, Agentic Product Management. The team works in close partnership with platform engineering, security, and compliance to ensure that the workflows we ship are safe, auditable, and scalable. A few principles shape how the team approaches its work: We start from outcomes, not from tasks. The first question on any engagement is what the process is for and what good looks like, not how to automate the current steps. We deliver working solutions, not recommendations. Every engagement ends with a prototype that real users can run. We design with audit trails. Every workflow is inspectable: which agent did what, against what input, with what reasoning. We expand trust deliberately. Agents start with bounded authority, and authority grows as evidence accumulates.

Requirements

Typically requires a minimum of 12 years of related experience with a Bachelor's degree or equivalent professional experience.
Demonstrated experience leading end-to-end process redesign and transformation engagements, with a track record of moving from diagnosis through implementation rather than stopping at recommendation.
Hands-on fluency with current AI tooling, including LLMs, coding assistants, and agent frameworks.
Strong process design, structured thinking, and stakeholder management skills, including the ability to navigate security, legal, and platform engineering review without losing the intent of the original design.
Comfort operating in environments where the problem is not yet well defined, and where part of the work is to define it.
Excellent written and verbal communication, with the ability to disagree productively and adjust as evidence evolves.

Nice To Haves

This experience may have been gained in: The digital, AI, or engineering arm of a major strategy or consulting firm — examples include BCG X (Gamma, Platinion), McKinsey QuantumBlack or McKinsey Digital, Bain Vector or Advanced Analytics, Deloitte AI & Engineering, Accenture Applied Intelligence — or a comparable practice
An internal AI, digital, or transformation team at a scaled technology company, with ownership of production deployments of agent-driven or AI-augmented workflows
A founding or early operating role at a startup building agent-native products
Familiarity with Cursor and Claude Code is preferred; experience with other orchestration platforms and agent frameworks is welcome.

Responsibilities

Partner with the team that owns the process. Build the relationship with the leader and the operators who do the work today. Conduct working sessions to understand inputs, outputs, decision points, tools, cycle time, and the moments where the work most often gets stuck. Produce a current-state view that the partner team agrees is accurate.
Define the outcome. Work with the partner-team leader to articulate what the process is actually trying to achieve and what good would look like. Identify which steps require human judgment, which serve regulatory or customer-facing purposes, and which exist because of tooling constraints. Quantify the prize: hours returned, cycle time reduced, quality improved, or capacity unlocked.
Design the future state. Produce a future-state design that uses agents to accelerate and augment the work, with humans engaged where their judgment, relationships, or accountability are essential. The design specifies the agents involved, the data they read and write, the tools they use, the decision rights they hold, the escalation paths to humans, and the controls that govern their behavior.
Build the prototype. Build a working version of the future-state design, hands-on, in code. Use Cursor, Claude Code, and the other AI development tools available to the team. The prototype is the artifact that proves the design is real and ready for first users.
Pilot with first users. Run the prototype with a defined set of first users in the partner organization. Train them, support them, gather structured feedback, and iterate. Document what works, what does not, and what needs to change before the workflow runs at scale.
Define the governance. Specify the audit trail, the cost controls, the escalation triggers, the access scope, and the trust-progression criteria that allow the workflow to operate inside NetApp’s security, legal, and compliance posture. Partner with the relevant review teams to get the workflow approved.
Instrument and measure. Define and capture the metrics that demonstrate the workflow is delivering the intended outcome. Produce the evidence the partner-team leader needs to defend and expand the new way of working.
Hand off to platform engineering. When the workflow is validated and the partner team has adopted it, transition the solution to platform engineering with the integration patterns, runbooks, and adoption playbook needed to operate it at NetApp scale.
Contribute patterns back. Capture reusable components from each engagement — agent designs, prompt patterns, governance templates, integration approaches — and contribute them to the team’s pattern library so future engagements move faster.