Note: The job is a remote job and is open to candidates in USA. NetApp is seeking a Senior Engineer on the AI Enablement team to enhance how the engineering organization works with AI. The role involves scaling engineering practices and developing autonomous agents that perform real tasks, requiring strong software engineering skills and the ability to navigate ambiguity.
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
Skills
- 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
Benefits
- Health Insurance
- Life Insurance
- Retirement or Pension Plans
- Paid Time Off
- Various Leave options
- Employee stock purchase plan
- Restricted stocks (RSU’s)
Company Overview
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