Staff SW Systems Engineer -Agentic Product Engineer

Extreme Networks•San Jose, CA

About The Position

Extreme Networks is a global networking leader with over 50,000 customers worldwide, providing cloud-driven networking solutions. The company is experiencing significant growth and is recognized as a Technology Leader in the Gartner Magic Quadrant. Extreme is committed to fostering an inclusive, people-first culture where diversity, inclusion, and equality are core values. This role is part of a newly formed, high-impact team focused on accelerating the adoption of AI-assisted ways of working across the company by designing and building internal AI-powered tools and systems. The team reports directly to senior executive leadership and offers engineers high ownership over their work. The Agentic Product Engineer / Staff SW Systems Engineer is a full-stack builder responsible for the end-to-end lifecycle of designing, implementing, testing, and deploying software, primarily using AI coding agents as their main execution tool. This role involves directing, evaluating, and iterating on agent-generated output to ship production-grade software with speed and quality. It is a non-specialized role where the engineer owns their surface from inception to production, handling backend APIs, data pipelines, frontends, CI/CD workflows, and cloud deployment without handoffs.

Requirements

8+ years of professional software engineering experience, with a track record of shipping production systems.
Proficiency in Python and TypeScript/JavaScript for backend services, pipelines, frontends, and automation.
CI/CD experience building pipelines and automated workflows (GitHub Actions).
Cloud fundamentals (AWS or Azure) sufficient to deploy developed solutions.
Production experience with AI coding agents (Claude Code, GitHub Copilot, Cursor, or equivalent), including agentic sessions beyond simple autocomplete.
Experience with agent frameworks such as Anthropic SDK, Microsoft Agent Framework, or equivalent.
Familiarity with MCP (Model Context Protocol).
Familiarity with RAG pipelines, Knowledge Graphs, and vector databases.
Experience building internal tooling adopted by large engineering organizations.

Responsibilities

Design, implement, test, and deploy internal AI-powered tools and systems.
Work with AI coding agents as the primary execution tool, directing, evaluating, and iterating on agent-generated output.
Own the full lifecycle of software development, including backend APIs, data pipelines, lightweight frontends, GitHub Actions workflows, and cloud deployment.
Build AI workflow infrastructure, including automation, integrations, and governance harnesses for AI-assisted development workflows.
Develop internal dashboards to help engineering teams track quality and progress on their AI-assisted work.
Build and integrate MCP servers and integrations to expose organizational knowledge to AI coding agents.
Operate with minimal supervision on new problem statements.