AI Infrastructure TPM: Rack-Scale GPU Validation Leader

Hardware FYI

Austin (TX)

In loco

USD 140.000 - 210.000

Tempo pieno

5 giorni fa

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Descrizione del lavoro

AMD Austin seeks a Technical Program Manager to lead AI infrastructure programs, guiding GPU platforms, rack-level bring-up, and cluster validation. You will coordinate hardware, firmware, networking, and validation teams to drive deployment readiness.

Strong PM skills and experience with AI-enabled automation are essential. You will interface with engineering leaders, manage risks, and drive data-driven status updates while advancing automation and AI-driven operational tools across hyperscale

Competenze

Experience leading hardware, systems, AI infrastructure, or datacenter programs through bring-up, validation, and deployment.
Technical understanding of GPU-based AI systems, server platforms, rack architectures, and data center infrastructure.
Experience with GPU cluster scale testing, system stress testing, validation, or large-scale systems validation.
Familiarity with rack-level bring-up, power, cooling, networking, cabling, and infrastructure readiness.
Experience coordinating cross-functional engineering teams and driving issues to closure without direct authority.
Exposure to fleet-scale validation or deployment of AI, cloud, HPC, or hyperscale infrastructure.
Understanding of infrastructure observability, telemetry, log analytics, failure analysis, and incident-management practices.
Knowledge of Agentic AI, AIOps, or AI-enabled automation in engineering/workflows.

Mansioni

Lead end-to-end program planning and execution for GPU-based AI infrastructure including server integration, rack bring-up, and deployment readiness.
Develop and maintain program schedules, dependency maps, risk tracking, and dashboards.
Drive alignment across GPU, CPU, firmware, BIOS/BMC, systems, networking, and lab infrastructure.
Coordinate multi-node and multi-rack scale testing, including test strategy and readiness gates.
Lead incident response for validation and deployment issues with cross-functional teams.
Establish and track operational metrics like MTTR, MTTD, MTTA, and fleet health.
Identify opportunities to apply Agentic AI and AIOps to engineering workflows and automation.