Principal Product Manager

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN · Product Management

Principal Product Manager for Azure AI Foundry and Azure ML, shaping strategy for AI platforms including model training, customization, deployment, and lifecycle management. Focuses on developer-facing APIs, SDKs, and enterprise-grade governance for large-scale AI workloads.

What you'd actually do

  1. Act as a senior contributor to platform strategy for Azure AI Foundry and Azure ML, helping shape multi-year investments across model training, customization, deployment, and lifecycle management.
  2. Drive alignment and progress across federated, cross-organizational initiatives, working with peer Principal PMs and multiple engineering teams on shared platform outcomes.
  3. Contribute to the definition and evolution of high-leverage platform abstractions (APIs, SDKs, workflows) that enable scalable adoption of GenAI and custom code training workloads.
  4. artner closely with senior engineering leaders to influence architectural direction, surface trade-offs, and ensure platform capabilities meet scale, reliability, and security expectations.
  5. Engage with strategic customers and internal stakeholders to gather insights, validate requirements, and translate learnings into durable, reusable platform capabilities.

Skills

Required

  • Product management or software engineering with substantial product ownership
  • Platform or infrastructure product experience
  • Ability to operate effectively in large, ambiguous, multi-team environments with shared ownership and complex dependencies
  • Technical depth in cloud platforms, distributed systems, or AI/ML infrastructure
  • Proven track record of influencing strategy, driving alignment, and delivering outcomes through collaboration rather than direct authority
  • Analytical and systems-thinking skills
  • Experience making high-quality decisions in fast-moving, evolving problem spaces

Nice to have

  • Building platforms for developers, data scientists, or ML engineers
  • Hands-on exposure to AI/ML or GenAI workloads, including model training, customization, or serving
  • Platforms operating at global scale, including multi-region or regulated enterprise environments
  • Product-led growth, extensibility, or ecosystem-oriented platform design

What the JD emphasized

  • platform or infrastructure products
  • large, ambiguous, multi-team environments with shared ownership and complex dependencies
  • AI/ML infrastructure
  • delivering outcomes through collaboration rather than direct authority

Other signals

  • AI platform strategy
  • developer-facing APIs and SDKs
  • enterprise-grade governance and reliability
  • custom code training
  • large-scale distributed compute