AI Infrastructure Supply Chain Lead

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

The AI Infrastructure Supply Chain Lead will be responsible for the strategic sourcing and end-to-end management of global AI infrastructure supply chains, focusing on high-performance compute and sovereign AI cloud platforms. This role requires deep technical knowledge of NVIDIA architectures and commercial acumen to navigate the semiconductor market, ensuring supply continuity and managing technical risks. Responsibilities include vendor management, contract negotiation, inventory management, and aligning procurement with technical roadmaps, with a focus on stringent security and 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.

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
  • NVIDIA Blackwell (GB200/GB300) architectures
  • NVLink
  • NVLink Switch systems
  • AI training
  • inference
  • stringent security
  • data residency
  • national compliance requirements