Sr. Product Manager- Windows Engineering Systems

Define and drive the vision, strategy, and roadmap for AI-assisted test failure analysis across Windows Engineering Systems. Build Copilot- and MCP-powered experiences that analyze test artifacts, logs, telemetry, and error signals to surface actionable root-cause insights directly within engineering workflows. Transform test failure analysis from a fragmented, manual investigation process into a streamlined experience that brings together relevant data, historical signals, and quality insights across engineering systems. Improve developer productivity by reducing investigation time, minimizing context switching, and integrating AI-assisted diagnostics into the engineering inner loop. Establish success metrics, AI evaluation frameworks, and quality measures focused on adoption, resolution effectiveness, signal quality, and reductions in no-repro outcomes. Partner across Engineering Systems, Quality Engineering, Azure DevOps, and Copilot platform teams to deliver trusted AI solutions that improve validation quality and release confidence Bachelor's Degree AND 5+ years experience in product/service/program management or software development. OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Bachelor's Degree AND 8+ years of experience in product management, program management, software engineering, or a related technical field, or equivalent experience. Experience delivering software products, engineering systems, developer tools, or platform services. Experience working on software quality, testing, validation, debugging, CI/CD systems, engineering infrastructure, or developer productivity solutions. Demonstrated ability to define product strategy and execute across multiple engineering organizations. Strong analytical skills and a data-driven approach to decision-making. Strong written, verbal, and executive communication skills. Experience building AI-powered products, copilots, agents, or LLM-based solutions. Experience defining AI evaluation frameworks, benchmarks, and quality measurement systems. Familiarity with Azure DevOps, CI/CD pipelines, test infrastructure, and engineering workflows. Experience designing or operating large-scale distributed systems and cloud services. Understanding of software validation, failure analysis, test automation, and debugging methodologies. Proven track record of leading ambiguous, cross-organizational initiatives that deliver measurable business impact.