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Forward Deployed Engineer, Agentic Product Management

Job in Morrisville, Wake County, North Carolina, 27560, USA
Listing for: NetApp
Full Time position
Listed on 2026-09-14
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 170000 - 220000 USD Yearly USD 170000.00 220000.00 YEAR
Job Description & How to Apply Below

Own Every Moment at Net App

At Net App, your ideas power innovation. We lead in intelligent data infrastructure—delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud.

Own Every Moment at Net App

At Net App, your ideas power innovation. We lead in intelligent data infrastructure—delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud.

Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, Net App is where your journey begins. Join teams that innovate to elevate, drive results, and excel together across every function.

Job Summary

This role engineers and ships production AI agents into the way a function already works. You embed inside functions across Net App’s product organization, working inside their environment, systems, and tools. You determine what to build and how, the technical approach and the architecture, by working the problem alongside them rather than from a written specification. You build and ship the system end to end and own that it works and holds up in production - reliable and safe on the function’s real workflows - before rotating to the next function.

The role sits within the Agentic Product Management team under the VP, Analytics Growth and AI Native Engineering.

The role combines three skillsets that usually live in three different people: the engineer who architects, writes, and ships the production system; the product-minded builder who can scope the right solution inside an unfamiliar function; and the consultant who can deliver inside a team that is not their own. The engineer is the spine - the person who writes the production code also embeds and scopes inside a function they have never worked in.

Building AI agents is the baseline; what you deliver is a working system the host function runs and trusts in production.

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.
Key Responsibilities
  • Embed inside a host function. Drop into a Product Group function such as product operations, program management, UX design, or product management - one that is not your own - for the duration of the engagement, and build the relationship with its leader and the operators who do the work today.
  • Scope the technical solution. Working from the problem the host team surfaces, decide what to build and how - the approach, the architecture, where an agent removes real toil, and where human judgment, relationships, or accountability must stay in the loop.
  • Build and ship the agent end to end. Put a working system into the host team’s live workflow using their real systems and data - prompts, tools, retrieval, sub-agents, and the integration code the deployment requires. The deliverable is production code running in the host team’s live workflow.
  • Make quality measurable. Build for non-deterministic systems: evaluations, golden sets, and regression checks that make the agent’s…
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