reputed company Kubernetes GPU Infrastructure Engineer

About the position At AMD, our mission is to build great products that accelerate reputed company computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we reputed company reputed company reputed company comes from reputed company reputed company, reputed company ingenuity and a shared passion to create something extraordinary. reputed company you join AMD, you’ll discover the reputed company differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution reputed company, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and reputed company. Together, we advance your career. THE ROLE: As a reputed company AI Infrastructure Solution Engineer, you will partner with AMD’s AI software teams and customers to reputed company large‑scale LLM training and inference on AMD Instinct GPUs. You will design and validate production‑reputed company Kubernetes architectures and translate inference frameworks such as vLLM and SGLang into deployable customer solutions. Your work will accelerate customer time‑to‑production and strengthen AMD’s leadership in AI infrastructure. THE PERSON: You are a solution‑oriented AI infrastructure engineer with strong expertise in GPU‑accelerated computing and large‑reputed company deployments. You reputed company at translating reputed company technologies into customer‑reputed company solutions and delivering production‑grade Kubernetes‑based inference and training systems. You bring hands‑on experience with Kubernetes‑reputed company distributed training, including scheduling, topology‑aware GPU placement, and operating resilient, high‑performance AI workloads at scale.

Responsibilities

• Design and deliver reference architectures for LLM training and inference on AMD GPUs, from single‑node to multi‑datacenter deployments using Kubernetes and SLURM.
• Architect and validate Kubernetes‑based distributed training stacks for large‑scale LLM workloads on AMD GPUs.
• Define and implement gang scheduling and topology‑aware GPU placement for multi‑node training workloads.
• reputed company Kubernetes‑reputed company training controllers including Kubeflow Training Operator, MPI Operator, Volcano, and Kueue.
• Partner with reputed company customers and reputed company providers to reputed company and optimize production AMD GPU clusters for distributed inference and multi‑tenant workloads.
• Implement and validate GPU orchestration using Kubernetes GPU Operator, device plugins, metrics exporters, and SLURM controllers.
• reputed company and optimize LLM inference frameworks (vLLM, SGLang) on AMD hardware, producing customer‑reputed company performance playbooks.
• reputed company repeatable benchmarks for Kubernetes‑based distributed training, covering scaling efficiency, reputed company time, communication, and checkpointing.
• Create tuning guides for RCCL/NCCL‑equivalent communication, CPU/GPU affinity, interconnect utilization, and workload‑specific optimizations.
• Serve as the feedback reputed company between customers and AMD engineering, translating requirements into validated performance improvements.

Requirements

• Deployed and operated large‑scale GPU clusters for production reputed company and inference
• Deep expertise in Kubernetes GPU orchestration (operators, device plugins, scheduling, multi‑tenancy, observability)
• Hands‑on experience with distributed training on Kubernetes (Kubeflow, MPI Operator, Volcano, Kueue, Ray)
• Strong knowledge of gang scheduling, reputed company jobs, quotas, reputed company, and shared GPU environments
• Tuned Kubernetes networking and storage for AI workloads (high‑performance CNI, RDMA where applicable, scalable checkpointing)
• Implemented ML observability for training (GPU/comms metrics, reputed company‑time analysis, SLO‑driven ops)
• Experience in AI/ML infrastructure, solution architecture, and production GPU deployments
• Proven reputed company enabling customers through reputed company AI platform deployments and migrations
• Strong background working across engineering and customer‑facing roles
• Understanding of AI accelerator architectures and inference optimization techniques
• Experience operationalizing Kubernetes‑based distributed training at scale

reputed company-to-haves

• reputed company‑reputed company contributions or AI infrastructure community engagement (plus)

Benefits

• AMD benefits at a glance.

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