System Solutions Architect -Large Clusters for AI & HPC workloads

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THE ROLE:

The AMD Datacenter GPU team is seeking an experienced System Solutions Architect to join our team focused on enabling large clusters for AI inference and training workloads.

THE PERSON:

The ideal candidate will be a technical expert in Kubernetes-based AI infrastructure with deep knowledge of datacenter-level solutions for AI inference and training. This role offers the opportunity to work at the cutting edge of AI infrastructure, solving complex technical challenges and helping customers implement transformative AI solutions at-scale.

KEY RESPONSIBILITIES:

• Lead customer technical discovery with data/ML, platform, and infrastructure stakeholders; map business goals to AI workloads (training, fine-tuning, inference) and success metrics.
• Assess current state (Kubernetes, GPUs/accelerators, storage, fabric, security, MLOps) and identify gaps, risks, and required POCs.
• Shape reference architectures for large AI clusters (rack design, GPU topology, RoCE/InfiniBand, NVMe/parallel FS, multi-tenant K8s) aligned to customer constraints (power, cooling, space).
• Translate model requirements into capacity and performance plans (tokens/sec, latency, throughput) and create BoMs with scaling paths.
• Partner with the business development and product teams to build ROI/TCO models (CapEx/OpEx, $/token, $/inference) and craft the value story.
• Support draft of technical sections of RFIs/RFPs; produce architecture diagrams, deployment plans, and implementation timelines.
• Partner with program & engineering teams to define POC success criteria, test plans, and exit reports
• Collaborate with product management to foster product roadmap improvements

PREFERRED EXPERIENCE:

• Kubernetes-based AI infrastructure design and implementation
• Load balancing strategies for high-volume inference workloads
• Specialized operators for AI inference at scale
• SLURM-based HPC cluster management for AI workloads
• Network design for high-throughput GPU clusters
• Storage architectures optimized for AI data pipelines
• Infrastructure automation and orchestration tools
• Performance optimization for large-scale inference deployments
• Knowledge of infrastructure-as-code tools (Terraform, Ansible, etc.)
• Familiarity with cloud-native observability and monitoring solutions
• Understanding of security considerations for AI infrastructure
• Solid years of experience designing and implementing large-scale infrastructure solutions
• Deep knowledge of Kubernetes and container orchestration technologies
• Hands-on experience with AI/ML workloads in production environments
• Strong understanding of datacenter networking and storage architectures
• Experience with GPU-accelerated computing environments
• Proven track record of creating technical documentation and reference architectures
• Excellent communication skills with the ability to explain complex technical concepts
• Experience working directly with customer technical teams

ACADEMIC CREDENTIALS:

• Bachelor's degree or higher in Computer Science, Electrical Engineering or closely related field.

LOCATION:

• San Jose, CA

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