Principal Product Manager

? Big, bold challenges at hyperscale ? High-impact product leadership in a high-stakes domain ⚡ Fast-paced execution across multiple engineering organizations ? A front-row seat to AIOps for M365 services...then this is your team. We're not just building AIOps tooling we're redefining how the most critical cloud services close the loop from risk to recovery, autonomously and safely. Come help us shape it! As a Principal Product Manager on the M365 AIOps team, You will define and drive product strategy for agentic incident management while fostering a culture of experimentation, safety, and operational excellence. Working at the intersection of large language models, distributed systems, and reliability engineering, you will operate in a fast-paced, highly technical, and highly ambiguous environment where agility, clarity, and bold thinking are essential. You will translate complex reliability and customer needs into scalable product strategy and set an example for AI-forward product leadership by balancing strategic vision with hands-on execution, and long-term thinking with rapid iteration. This opportunity will allow you to grow as a product leader while working on capabilities that shape the future of how M365 stays reliable and self-healing. You will gain hands-on experience shaping the architecture of autonomous agentic AIOps systems at a scale which few teams in the industry can offer. You will deepen your technical product acumen and strengthen your ability to drive clarity across complex, cross-organizational initiatives spanning M365's most critical services. Bachelor's Degree AND 8+ years experience in product/service/program management or software development These requirements include but are not limited to the following specialized security screenings: Bachelor's Degree AND 12+ years experience in product/service/program management or software development OR equivalent experience. 4+ years experience taking a product, feature, or experience to market (e.g., design, addressing product market fit, and launch, internal tool/framework). 6+ years experience improving product metrics for a product, feature, or experience in a market (e.g., growing customer base, expanding customer usage, avoiding customer churn). 6+ years experience disrupting a market for a product, feature, or experience (e.g., competitive disruption, taking the place of an established competing product). Experience with M365 services, AKS (Azure Kubernetes Service) or comparable hyperscale services. Background in autonomous/self-healing systems, control planes, or safe-automation platforms. Familiarity with chaos engineering, failure-mode analysis, and blast-radius modeling. 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, real incident patterns, and measurable reliability outcomes. Define how AI discovers meaningful 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—not simply flag unusual behavior. Establish standards for agents that retrieve supporting telemetry, reason across signals, 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 within responder workflows. Enable natural-language investigation, context-aware evidence retrieval, and guided next steps so engineers can interrogate conclusions, collaborate with AI, and move from questions to informed decisions faster. 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 so agents demonstrate restored service and customer experience, not merely successful action completion. Convert responder feedback, investigation traces, post-incident reviews, and mitigation outcomes into reusable knowledge, new evaluation cases, and improvements to models and agent workflows. Validate those improvements before expanding autonomy, making diagnosis and recovery more effective without compromising safety.