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AI Cluster Technical Program Manager – Validation, Debug & Agentic AI

AMD· Engineering
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Hybrid (Austin, TX)$162.6k - $244kFull-timePosted 2mo ago

About the role

Technical Program Manager leading AI cluster engineering programs with a focus on GPU platforms, rack-level solutions, and AI cluster validation to enable hyperscale and enterprise AI deployments.

Responsibilities

  • Define, plan, and drive program plans for AI infrastructure systems validation and readiness, including server integration, rack bring-up, and cluster-scale deployment readiness.
  • Create and maintain core PM artifacts: schedules, dependency maps, resource forecasts, risk/issue logs, and program dashboards/status reports.
  • Identify and drive mitigation plans for issues/risks, including cross-team escalations and corrective actions across multiple engineering areas.
  • Drive regular execution reviews with engineering teams and provide concise, data-driven updates to senior leadership.
  • Own program execution for GPU-based AI platforms, spanning system bring-up, qualification, scale readiness, and deployment validation across server, rack, and cluster levels.
  • Drive alignment across GPU, CPU, firmware, BIOS/BMC, and system teams to ensure readiness for scale testing and customer workloads.
  • Track platform issues, and debug dependencies; ensure risks are clearly documented, owned, and mitigated.
  • Own program planning and execution for multi-node and multi-rack scale testing, including test strategy, scheduling, coverage tracking, and readiness gates.
  • Lead end-to-end delivery of rack-level AI solutions, including compute trays, switch trays, cabling, power, cooling, and management infrastructure.
  • Ensure rack bring-up plans are executable, resourced, and gated with clear entry/exit criteria across EVT, DVT, and scale phases.
  • Drive coordination across lab operations, infrastructure, and engineering teams to unblock rack access, power, networking, and test readiness.
  • Partner with scale, performance, and automation teams to ensure workloads, stress tests, and regressions plans are ready before hardware arrives.
  • Act as the execution lead for platform debug, coordinating across engineering teams to ensure fast triage, root-cause analysis, and resolution of system-level issues.
  • Track high-impact failures through debug forums ensuring clear ownership and closure plans.
  • Balance debug depth vs. program timelines, escalating tradeoffs when needed and ensuring leadership has a clear view of risk and impact.
  • Lead end-to-end system rack and cluster-level debug activities during validation, deployment, and fleet operations, driving rapid fault isolation and root-cause analysis across hardware, firmware, networking, and software domains.
  • Own incident management for critical deployment and validation issues, coordinating cross-functional war rooms and executive communications to ensure timely resolution.
  • Define key operational metrics such as MTTD, MTTR, MTTR, incident recurrence rates, and fleet health indicators.
  • Drive post-incident reviews (PIRs), RCA, corrective actions, and long-term preventive measures to reduce Sev1/Sev2 incident recurrence and improve fleet reliability and deployment readiness.
  • Champion Agentic AI and AIOps solutions for automated incident triage, log analysis, root-cause identification, and operational workflow automation at scale.
  • Identify opportunities to leverage Agentic AI capabilities across validation and debug workflows.
  • Drive development and rollout of AI agents for automated triage, log analysis, root-cause identification, knowledge retrieval, test orchestration, and incident management.
  • Partner with data engineering teams to integrate AI-powered operational tools.
  • Establish success metrics and measurable business impact for agent-based automation initiatives.
  • Champion AI-first operational workflows across engineering organizations.

Qualifications

  • Experience leading complex hardware or AI infrastructure programs with ownership across bring-up, validation, and deployment phases.
  • Strong technical understanding of GPU-based AI systems, rack architectures, and datacenter infrastructure.
  • Proven ability to manage ambiguity, drive debug execution, and lead cross-functional teams without direct authority.
  • Strong written and verbal communication skills, including executive-level status reporting.
  • Proficiency with program management and execution tools (Jira, Confluence, dashboards, Excel/PowerPoint).

Skills

Required:Program managementGPU-based AI systemsRack-level bring-up and validation
Nice to have:Debug and incident management
Last updated Oct 15, 2026. Interested? Apply on the company site.
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