About The Position

Microsoft Specialized Clouds (MSC) delivers secure, compliant, and highly reliable cloud environments for mission-critical customers. The Senior Technical Program Manager will lead AI transformation for MSC engineering, driving adoption of agentic engineering across the software development lifecycle—from requirements and design through coding, code review, testing, security, deployment, and operations—with the goal of measurable improvements in execution and delivery outcomes. The role owns the strategy and execution of AI transformation across MSC. Working with the AI v-team led by the MSC CTO and leaders across the organization, the program manager will translate priority engineering scenarios into scalable AI-enabled capabilities; establish governance and operating rhythms; drive organizational change; and use evidence and feedback to improve planning, engineering productivity, decision quality, and delivery performance. The role works across engineering, product, data science, finance, security, compliance, and operations to build trusted solutions and influence decisions with appropriate human oversight.

Requirements

  • Bachelor's degree and 4+ years of experience in engineering, product/technical program management, data analysis, or product development, or equivalent experience.
  • 2+ years of experience managing cross-functional and/or cross-team projects.
  • Must pass the Microsoft Cloud background check upon hire or transfer and every two years thereafter.
  • U.S. citizenship is required and will be verified via a valid passport due to citizenship-based legal restrictions applicable to support for U.S. government agency customers.
  • 3+ years of experience leading complex, cross-functional technical programs from definition through delivery and adoption.
  • Demonstrated experience building automation, data, and analytics to improve processes, product experiences, and evidence-based decision-making.
  • Experience delivering AI-powered capabilities or solutions from concept through deployment and adoption, and sustaining and iterating on them in production as AI capabilities evolve.
  • Technical and systems-thinking skills, including managing requirements, dependencies, risks, data, telemetry, security, and measurable outcomes.
  • Written and verbal communication skills, including influencing senior leaders, driving decisions, and aligning engineering and business teams across organizational boundaries.
  • Experience building AI-powered capabilities such as copilots, agents, recommendation systems, forecasting tools, or workflow automation from concept through deployment and adoption.
  • Familiarity with product analytics, process mining, causal analysis, experimentation, capacity modeling, operations research, and model evaluation.
  • Experience operationalizing responsible AI practices, including data governance, privacy, security, compliance, and human-in-the-loop review mechanisms.
  • Ability to engage deeply with technical teams on architecture, data models, APIs, telemetry, integration, reliability, and release readiness.

Nice To Haves

  • Coding or prototyping experience.
  • Experience delivering complex technical programs in cloud infrastructure, specialized or sovereign clouds, government environments, or other regulated industries.

Responsibilities

  • Partner with the MSC CTO office and AI v-team to set priorities and maintain an outcome-focused roadmap for AI-first engineering.
  • Identify high-impact opportunities to improve engineering workflows, assess emerging AI capabilities and external industry best practices against organizational needs, and turn selected opportunities into actionable initiatives with clear outcomes.
  • Partner with engineering teams and v-team workstream leads to align AI-enabled workflows, agentic capabilities, and shared engineering practices with organizational and team needs and high-impact outcomes.
  • Support AI optimization experiments, challenge existing approaches, and help turn successful approaches into sustained improvements in how teams plan, build, review, and operate while maintaining security, quality, and reliability.
  • Partner with workstream and analytics leads to define success measures and assess improvements in engineering productivity, quality, and operational outcomes.
  • Use insights and feedback to inform priorities and continuous improvement.
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