AI Infrastructure Supply Chain Lead

Armada Armada · Enterprise · Bellevue, Sunset Corporate · R&D - Edge Hardware

Lead the global AI infrastructure supply chain, focusing on sourcing high-performance compute (HPC) and sovereign AI cloud platforms. Requires deep technical fluency in NVIDIA architectures and commercial acumen for semiconductor market navigation. Responsibilities include strategic sourcing, vendor management, commercial leadership, technical risk mitigation, inventory management, cross-functional roadmap alignment, system integration support, and ensuring compliance for Sovereign AI deployments.

What you'd actually do

  1. Identify, vet, and manage Tier 1/2 OEMs and regional distributors for high-density servers, network gear, and cabling. Build a resilient multi-vendor strategy to eliminate single points of failure.
  2. Drive end-to-end contract lifecycles, including Master Purchase Agreements (MPAs), Service Level Agreements (SLAs), and complex warranty/support negotiations.
  3. Monitor global semiconductor trends to mitigate long-lead-time risks. Support Solution Engineering by ensuring "just-in-time" inventory of mission-critical hardware (GPUs, NICs, Switches).
  4. Partner with Systems Engineering and Architecture teams to translate technical specs into scalable, multi-year procurement roadmaps.
  5. Oversee the procurement and delivery of integrated components, including NVIDIA Grace CPUs, NVLink, InfiniBand, and ConnectX-8 technologies.
  6. Architect procurement workflows that satisfy stringent security, data residency, and national compliance requirements for Sovereign AI cloud deployments.

Skills

Required

  • 5+ years of experience in high-performance computing (HPC) or hyperscale datacenter procurement environments.
  • Deep understanding of NVIDIA Blackwell (GB200/GB300) architectures, including the performance characteristics of NVLink and NVLink Switch systems for AI training and inference.
  • Foundational understanding of the hardware-software stack, including operating systems, device drivers, and firmware versioning.
  • Ability to bridge the gap between technical engineering requirements and executive-level financial/commercial constraints.

Nice to have

  • Proven experience navigating regional distributor landscapes in APAC and EMEA.
  • Experience with rack-level integration for modular or Edge datacenter deployments.
  • Practical knowledge of how hardware specifications impact specific AI workloads (LLM training vs. low-latency inference).

What the JD emphasized

  • NVIDIA architectures
  • semiconductor market
  • Sovereign AI
  • compliance requirements