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

THE ROLE:

We are seeking a Technical Program Manager to lead execution of AI cluster engineering programs withdeepfocus on GPU platforms, rack-level solutions, and AI Cluster validation. This roleis responsible fordriving end-to-end delivery from GPU + server integration through rack bring-up, scale testing, failure analysis, and system debug closure, ensuring platform readiness for hyperscale and enterprise AI deployments.

This roleoperatesat the intersection of hardware, firmware, networking, and scale-test execution, and requires strong technical depth combined with disciplined program execution.

THE PERSON:

You are a hands-on TPM who thrives in complex, fast-moving ecosystems, and can connect deep technical details to crisp program plans, executive reporting, and customer outcomes.You are comfortable driving execution in bring-up and EVT/DVT/PVT working closely with engineers to root-cause issues, unblock debug, and make data-driven tradeoffs to keep programs moving. You bring urgency, ownership, and clarity to ambiguous problem spaces and can communicate effectively fromlabfloor to executive review.

KEY RESPONSIBILITIES:

Program Leadership & Execution

• 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 andmaintaincore PM artifacts: schedules, dependency maps, resource forecasts, risk/issue logs, and program dashboards/status reports.

• Identifyand 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.

GPU & Platform Execution

• 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.

AI Rack / Cluster Validation

• 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.

Debug, Failure Analysis & Risk Management

• 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 (GPU, HSIO, FW, rack, network) throughdebugforums 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.

System Rack Debug & Incident Management

• Lead end-to-end systemrackand 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 ensuretimelyresolution.

• Define key operational metrics such as Mean Time to Detect (MTTD), Mean Time to Mitigate (MTTM), Mean Time to Resolution (MTTR), incident recurrence rates, and fleet health indicators.

• Drive post-incident reviews (PIRs), root-cause analysis (RCA), corrective actions, and long-term preventive measuresto 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

Agentic AI Transformation

• Identifyopportunities toleverageAgentic 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
  • 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.

REQUIRED 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.

• Proficiencywith program management and execution tools (Jira, Confluence, dashboards, Excel/PowerPoint).

PREFERRED QUALIFICATIONS:

• Hands-on experience with GPU cluster scale testing, system stress, or performance validation.

• Familiarity with rack-level bring-up, power/cooling constraints, networking, and failure modes at scale.

• Experience working through hardware/firmware debug cycles in pre-production or customer-facing environments.

• Experience managing fleet-scale validation and deployment of AI, cloud, HPC, or hyperscale infrastructure.

• Strong understanding of infrastructure observability, telemetry pipelines, log analytics, and incident management frameworks.

ACADEMIC CREDENTIALS:

• Bachelor's or master'sdegreesystems, EE, CS, or related engineering discipline.

• PMP, Scrum Master, or equivalent program management training.

LOCATION: Austin, TX