AI Software Engineer

Own Every Moment at NetApp At NetApp, 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, NetApp is where your journey begins. Join teams that drive results, innovate to elevate, and excel together across every function. Job Summary As an Sr Engineer on the AI Enablement team, you will be a hands-on builder and a force multiplier for how the broader Keystone engineering org works with AI. You'll split your time between scaling engineering practice and shipping autonomous agents that do real work — not prototypes that stall after a demo. This role requires strong software engineering fundamentals, comfort operating with ambiguity on a fast-moving small team, and the judgment to know when an AI-generated answer is good enough to ship versus a production risk. Job Responsibilities • Scale engineering with AI • Drive spec-driven, agentic-IDE development workflows (Cursor, Claude Code) across the team, from spec to implementation to review, so AI usage translates into measurable velocity, not just novelty. • Define and evolve quality gates for AI-generated code: what changes in review, CI, and merge criteria when a large share of PRs are AI-assisted. • Build and maintain shared skills, prompts, and MCP tooling that other Keystone engineers reuse, reducing redundant AI infrastructure across teams. • Build production AI agents on the enterprise stack • Design and ship agent workflows (planner vs. fixed-workflow, state/retries, human-in-the-loop checkpoints) that automate real toil • Build well-scoped MCP tools/integrations against enterprise systems with clear schemas, auth boundaries, idempotency, and explicit rules on what data never reaches the model. • Own evals and guardrails for agents in production: offline golden sets, online sampling, canary/rollback paths, and hard checks on numeric/PII/outbound-send correctness — distinguishing tool failures from model failures. • Design for operational readiness from day one: what breaks under a 10× usage spike before quarter close, what the kill switch looks like when an agent gets a fact wrong, and how incidents get triaged. Job Requirements • Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent, relevant experience. • 8+ years of professional software development experience, with proven ownership of at least one system or domain end-to-end. • Production experience with LLMs - prompt engineering, agent development, and evaluation frameworks. • Hands-on experience building agent harnesses: tool calling, state/session management, retries, guardrails, and stop conditions for long-running agent workflows. • Deep, hands-on proficiency in Python/Go/Java • Experience with agent/orchestration frameworks (LangChain, LangGraph, or equivalent) and MCP or similar tool-calling standards, integrated with enterprise systems • Working knowledge of AI-native, spec-driven development workflows (agentic IDEs like Cursor/Claude Code) and defining quality gates for AI-generated code. • Strong problem decomposition and stakeholder navigation - able to scope an ambiguous cross-functional ask into a shippable plan and own it end-to-end, forward-deployed-engineer style. Education IC - Typically requires a minimum of 8 years of related experience.Mgr & Exec - Typically requires a minimum of 6 years of related experience. Compensation: The target salary range for this position is 170,000 - 253,000 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. The range is based on 'On Target Earnings' (OTE) representing the total potential earnings, which is the sum of the base salary and potential commission earned when performance targets are achieved. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, employee stock purchase plan, and/or restricted stocks (RSU's). These offerings are subject to regional variations and governed by local laws, regulations, and company policies. We will provide detailed information about the specific benefits for your region during the recruitment process. At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person