AI Ops Engineering Manager

** Overview*
* Are you a customer-obsessed, AI-curious problem-solver who thrives in an inclusive, collaborative global team? Join Engineering Operations (Eng Ops), the organization driving operational excellence across the Microsoft Cloud to strengthen quality, reliability, security, and customer trust. Our mission is to prevent issues before they happen by turning operational signals into action through AI-powered automation, enabling measurable customer impact at cloud scale.

Our culture of empowerment, inclusion, and growth mindset defines how we work.

Within Eng Ops ACES, the Agent Fleet Operations (AFO) team is seeking an AI-first Engineering Manager to join Cloud Operational Demand Engineering (CØDE) and help advance our mission of driving operational demand toward zero. We are looking for a technical leader who combines deep engineering expertise with a learn-it-all mindset, is passionate about applying AI to solve real customer problems and is energized by building high-performing teams and systems that scale impact far beyond individual effort.

In this role, you will lead team-building and operating production AI solutions that reduce operational demand and improve customer outcomes. You will be responsible for people leadership, engineering execution, prioritization, and production operations, partnering closely with senior technical leaders to translate strategy into reliable, scalable capabilities. Success requires fostering an inclusive environment where engineers can move quickly, experiment, learn continuously, and deliver high-quality solutions that transform how the Microsoft Cloud operates.

Your Role:

Lead a hands-on AI engineering squad that builds and operates autonomous resolution agents for Broad Commercial customers. You will own engineering execution, production excellence, and business outcomes, not just delivery plans. This role requires a leader who has personally built and shipped production-scale agentic AI systems and can scale that expertise through a high-performing engineering team.

Team charter

Build, operationalize, and scale AI-native support experiences for Broad Commercial customers by transforming supportability knowledge into reusable, versioned, testable, and measurable assets that power product experiences, support engineers, and autonomous agents. The team reduces support demand, engineering toil, and time-to-resolution through AI-driven resolution capabilities that continuously learn from customer signals, unresolved cases, and operational insights.

** Responsibilities*
* Key responsibilities

+ Build and lead an AI engineering squad

+ Hire, develop, and lead engineers building AI-powered solutions that reduce support demand, accelerate resolution, and improve customer satisfaction. Set a high technical bar, remain close to architecture and evaluation outcomes, and create the conditions for rapid innovation without compromising quality or reliability.

+ Own engineering execution for Agentic AI solutions

+ Own end-to-end delivery of autonomous AI systems, including orchestration, reasoning, retrieval, action execution, and human escalation workflows. Drive capabilities from prototype through production across support surfaces while partnering effectively across dependent engineering organizations.

+ Set technical direction and engineering standards

+ Shape the technical foundation for agent development by defining architectures, design patterns, evaluation frameworks, and reusable platform capabilities. Establish standards for prompts, tool interfaces, testing, versioning, code quality, and safe deployment practices to ensure agent behavior remains reliable, observable, and scalable.

+ Own production health and reliability

+ Run the service. Own availability, latency, cost per resolution, regression detection, incident response, and the on-call discipline for a fleet of agents acting autonomously in the customer path. Build the observability needed to explain why an agent did what it did, and drive durable fixes rather than repeated mitigations.

+ Build and maintain evaluation systems

+ Own the evaluation framework that determines whether agents are safe and effective in production. Build and maintain golden datasets, rubrics, judge agents, and offline/online evaluation pipelines. Use evaluation signals to gate releases and continuously improve quality through production feedback loops.

+ Drive measurable business impact

+ Own key business outcomes, including support demand reduction,…