Principal Product Manager

Microsoft•Redmond, WA

About The Position

The Principal Product Manager on Microsoft 365’s AIOps team defines and drives product strategy for agentic incident management. The team is building autonomous systems that continuously detect risk, reason over live signals, and drive recovery actions to help keep M365 services reliable at hyperscale. The role operates at the intersection of large language models, distributed systems, and reliability engineering in a fast-paced, technically complex, and ambiguous environment. The position translates customer and reliability needs into scalable product strategy and balances long-term vision with hands-on execution and rapid experimentation. It shapes how AI agents detect and diagnose incidents, communicate evidence and customer impact, guide responders, and safely plan or orchestrate recovery. The role also establishes safeguards and evaluation practices so increased autonomy improves diagnosis and recovery without compromising service safety.

Requirements

Bachelor’s degree and 8+ years of experience in product, service, or program management, or software development, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

Bachelor’s degree and 12+ years of experience in product, service, or program management, or software development, or equivalent experience.
4+ years of experience taking a product, feature, or experience to market, including design, product-market fit, and launch.
6+ years of experience improving product metrics for a product, feature, or experience in a market.
6+ years of experience disrupting a market for a product, feature, or experience.
Experience with M365 services, AKS (Azure Kubernetes Service), or comparable hyperscale services.
Background in autonomous or self-healing systems, control planes, or safe-automation platforms.
Familiarity with chaos engineering, failure-mode analysis, and blast-radius modeling.

Responsibilities

Define the vision for intelligent agents that detect failures, reason over telemetry, and drive recovery.
Prioritize investments in AI-powered signal intelligence, diagnosis, and autonomous workflows based on customer needs, incident patterns, and measurable reliability outcomes.
Define how AI discovers anomalies, learns historical baselines, compares affected and healthy systems, and connects evidence across logs, metrics, and distributed traces.
Enable agents to generate and test hypotheses, identify emerging failure patterns, and explain customer impact.
Establish standards for agents to retrieve telemetry and produce traceable findings with clear provenance, scope, timing, confidence, and limitations.
Define how AI validates hypotheses, surfaces conflicting evidence, and distinguishes plausible explanations from verified causes.
Define how agents proactively surface, rank, and explain findings in responder workflows.
Enable natural-language investigation, context-aware evidence retrieval, and guided next steps for engineers.
Translate AI-generated diagnoses into agents that plan, recommend, and orchestrate recovery actions, from operator-guided execution to governed autonomy.
Define approval gates, execution safeguards, rollback controls, and continuous health verification.
Convert responder feedback, investigation traces, post-incident reviews, and mitigation outcomes into reusable knowledge, evaluation cases, and improvements to models and agent workflows.
Validate improvements before expanding autonomy, ensuring diagnosis and recovery become more effective without compromising safety.