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/ML and GenAI platforms including training, deployment, monitoring, and governance. Focuses on developer-centric AI platforms enabling organizations to build, deploy, and operate AI systems at scale.

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. Partner 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
  • Strong technical depth in cloud platforms, distributed systems, or AI/ML infrastructure
  • Proven track record of influencing strategy, driving alignment, and delivering outcomes through collaboration
  • Strong analytical and systems-thinking skills
  • Experience making high-quality decisions in fast-moving, evolving problem spaces

Nice to have

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

What the JD emphasized

  • platform strategy
  • multi-year investments
  • model training
  • customization
  • deployment
  • lifecycle management
  • platform abstractions
  • GenAI
  • custom code training workloads
  • architectural direction
  • scale
  • reliability
  • security expectations
  • strategic customers
  • internal stakeholders
  • platform capabilities
  • AI/ML infrastructure
  • large, ambiguous, multi-team environments
  • shared ownership
  • complex dependencies

Other signals

  • AI platforms
  • ML platforms
  • GenAI workloads
  • custom code training
  • large-scale distributed compute
  • developer-facing APIs and SDKs
  • enterprise-grade governance and reliability