AI Platform Architect

• End-to-End Platform Architecture: Define the holistic architecture for highly clustered AI environments, ensuring zero-bottleneck data flow between parallel storage systems, AI compute nodes, and ultra-high-bandwidth network fabrics. - Workload Orchestration: Influence the strategy for AI workload scheduling and orchestration, utilizing tools like Kubernetes or Slurm to manage distributed training jobs, model check-pointing, and inference serving at massive scale. - Full-Stack Optimization: Profile and eliminate system-level bottlenecks across the entire AI pipeline, tuning everything from deep learning frameworks (PyTorch, DeepSpeed, etc.) down to OS-level NUMA pinning and I/O scheduling. - Hardware-Software Co-design: Work closely with software, firmware, and OS engineering to influence platform design, ensuring the software stack fully exploits underlying hardware capabilities, including complex ARM mesh interconnects (RNI, HNF, SNF) and advanced merchant silicon features. - Silicon Influencing Strategy: Drive the 3-to-5-year technical vision for the AI platform. Collaborate closely with subject matter experts in processor, memory, storage, GPU, thermal, mechanical, BIOS, and Manageability disciplines to define requirements specifications to communicate and present to internal and external silicon teams to influence features, optimized board routing guidelines, power and thermal targets, and the correct feeds and speeds for a competitive AI platform. This will require a deep knowledge of the AI industry and significant market competitive analysis including TCO (OPEX / CAPEX) analysis of new technologies. Candidate Profile

Essential:

• Experience: Demonstrated ability in systems engineering, cloud architecture, or HPC, hardware engineering with at least 4+ years functioning as a Lead or Principal Architect for large-scale AI or machine learning platforms. - Distributed AI Frameworks: Deep practical knowledge of how large models are trained and deployed, including data/tensor/pipeline parallelism and the infrastructure requirements of modern LLM architectures. - Systems Interconnects: Authoritative understanding of system-level bottlenecks and data pathways, including deep familiarity with PCIe Gen 5/6, NVMe namespaces, and RDMA (RoCEv2/InfiniBand) integration. - Orchestration & Containerization: Experience with container orchestration platforms and infrastructure-as-code (IaC) tailored for GPU-heavy bare-metal and cloud environments. - Cross-Domain Leadership: Exceptional ability to bridge the gap between AI researchers/data scientists and low-level hardware/CPU/memory/storage/GPU/network engineers, translating model requirements into strict infrastructure specifications. Ability to generate Platform engineering requirement specifications that can be used to guide and influence future silicon designs. Desirable

• Rack scale GPU AI Platforms experience: Hands on experience with rack-as-a-system

• AI platforms that integrate all the latest networking, cooling, and GPU technologies currently present in the market. - Software / Scripting experience: Working knowledge of scripting language such as Python/JSON to characterize workloads on bare metal AI compute systems to expose issues with current Neural engine silicon solutions.