Lead HPC & AI Infrastructure Architect

At AMD, we believe technology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.

Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger- technology that moves the world forward. Join us and, together, we'll advance your career.

We are seeking a Lead HPC & AI Infrastructure Architect to own the architecture and technical direction of large‑scale CPU and GPU platforms across on‑premises infrastructure, public cloud, and specialized GPU cloud providers. You will translate scientific computing, engineering, and AI requirements into secure, reliable, and cost‑effective platforms for tightly coupled HPC workloads, distributed model training, and inference services.

This hands‑on leadership role spans design, workload validation, deployment, and lifecycle management. You will guide infrastructure engineering and SRE teams and partner with researchers, AI engineers, cloud providers, and hardware vendors to deliver production‑ready platforms.

You combine deep systems expertise with practical architectural judgment. You take ownership of complex technical problems, validate decisions with measurable results, and communicate tradeoffs clearly. You collaborate across engineering, operations, security, and research teams; mentor engineers; and balance performance, reliability, security, delivery timelines, and cost. You can independently guide initiatives from requirements through production acceptance.

• Own the HPC and AI infrastructure roadmap, reference architectures, and standards for CPU/GPU clusters. Define workload placement across bare metal, virtual machines, Slurm, and Kubernetes.
• Lead designs covering CPU/NUMA and GPU topology, accelerator interconnects, networking, storage, and identity. Establish validated hardware, firmware, driver, and runtime baselines.
• Architect RDMA‑capable fabrics and storage tiers. Validate collective communication, parallel I/O, dataset delivery, and checkpoint performance at target scale.
• Design resource sharing through Slurm accounting, fair‑share, quotas, QoS, preemption, and tenant isolation. Enable reproducible environments, self‑service onboarding, and inference capacity planning.
• Standardize provisioning, configuration, and fleet upgrades through infrastructure as code, automation, and CI/CD; manage images, containers, and configuration drift.
• Define production acceptance criteria, service‑level objectives, and fleet observability. Partner with SRE on fault isolation, job recovery, checkpoint/restart, incident analysis, and capacity planning.
• Benchmark HPC and distributed AI workloads; improve scaling efficiency, throughput, inference latency, GPU utilization, and cost efficiency through bottleneck analysis.
• Embed federated identity, least‑privilege access, tenant and network isolation, secrets management, controlled container privileges, audit logging, and vulnerability management.
• Lead architecture reviews, proofs of concept, provider evaluations, and technical escalations. Document tradeoffs, mentor engineers, and align delivery with stakeholders and infrastructure partners.

• Typically 10+ years in infrastructure, cloud engineering, or distributed systems, including 5+ years designing and delivering production HPC or GPU platforms; equivalent demonstrated experience is welcome.
• Ownership of large‑scale cluster deployments involving hundreds of nodes or comparable complexity, with measurable…