Technical Support Engineer - AI Lead

Microsoft Microsoft · Big Tech · Redmond, WA +4 · Technical Support Engineering

This role is for an AI Lead Technical Support Engineer who will operate as a recognized authority, driving the strategic agenda for AI programs, defining Responsible AI and model-governance standards, and overseeing complex AI initiatives. The role requires expertise across the LLM and agentic stack, with a focus on evaluation and governance frameworks.

What you'd actually do

  1. You will operate as a recognized authority: driving the strategic agenda for AI programs across divisions, defining organization-wide Responsible AI, evaluation, and model-governance standards, and holding teams accountable to performance and safety requirements throughout the AI lifecycle.
  2. You will be the definitive voice on program goals and prioritization across boundaries, orchestrate complex AI initiatives that span organizations, influence engineering and executive stakeholders on architecture and roadmap, and oversee delivery end to end.
  3. You will invest heavily in thought leadership, shaping the discipline and mentoring other program managers.

Skills

Required

  • Bachelor's Degree in Computer Science, Information Technology (IT), or related field AND 10+ years technical support, technical consulting experience, or information technology experience
  • Ability to meet Microsoft, customer and/or government security screening requirements
  • U.S. citizenship

Nice to have

  • Bachelor's Degree in Computer Science, Information Technology, or related field AND 15+ years of technical support, technical consulting experience, or information technology experience
  • 15+ years in technical program or product management with a strong record of principal-level AI/ML program ownership
  • Sustained record of organization-wide strategic impact in AI and industry credibility
  • Proven history of defining AI evaluation and governance frameworks that get adopted broadly
  • Executive-level stakeholder management and the judgment to set direction under significant ambiguity and technical risk
  • Expert fluency across the LLM and agentic stack, data and evaluation strategy, model-risk and Responsible AI governance, and the cost, latency, and quality economics of production AI systems

What the JD emphasized

  • principal-level AI/ML program ownership
  • Sustained record of organization-wide strategic impact in AI and industry credibility
  • Proven history of defining AI evaluation and governance frameworks that get adopted broadly
  • Expert fluency across the LLM and agentic stack, data and evaluation strategy, model-risk and Responsible AI governance, and the cost, latency, and quality economics of production AI systems

Other signals

  • driving the strategic agenda for AI programs across divisions
  • defining organization-wide Responsible AI, evaluation, and model-governance standards
  • holding teams accountable to performance and safety requirements throughout the AI lifecycle
  • orchestrate complex AI initiatives that span organizations
  • influence engineering and executive stakeholders on architecture and roadmap
  • oversee delivery end to end
  • Proven history of defining AI evaluation and governance frameworks that get adopted broadly
  • Expert fluency across the LLM and agentic stack, data and evaluation strategy, model-risk and Responsible AI governance, and the cost, latency, and quality economics of production AI systems