Solutions Architect - Nvidia Cloud Partners

NVIDIA NVIDIA · Semiconductors · Germany +4 · Remote

NVIDIA is seeking an experienced Solutions Architect to bridge the gap between design and deployment of large-scale AI and HPC GPU infrastructure. This role involves collaborating with NVIDIA Cloud Partners, providing technical support to customers throughout their GPU infrastructure lifecycle, and driving end-to-end technology solution integration. The architect will also offer recommendations based on customer feedback and prepare technical content.

What you'd actually do

  1. Collaborating with NVIDIA Cloud Partners to create, implement, and put into operation NVIDIA's innovative hardware and software solutions.
  2. Partner with Sales Account Managers and other business leads to identify and secure business opportunities for NVIDIA products and solutions.
  3. Act as the primary technical support for customers during the development, construction and production of extensive GPU cloud infrastructure through whole customer lifecycle.
  4. Conduct regular technical customer meetings for project/product details, feature discussions, intro to new technologies, and debugging sessions.
  5. Work with customers to build PoCs for solutions to address critical business needs by building out networking and compute infrastructure.

Skills

Required

  • BS/MS/PhD in Mechanical/Electrical Engineering, or other Engineering fields or equivalent experience.
  • 5+ years of Solution Engineering (or similar Sales Engineering, Cloud Engineering) experience working directly with partners and customers.
  • Experience crafting and deploying large-scale cluster environments.
  • Practical expertise in datacentre design, development, and execution for AI and HPC.
  • Efficient time management and capable of balancing multiple tasks.
  • Ability to communicate ideas clearly through documents, presentations, etc.

Nice to have

  • Practical familiarity with large datacentre design, power distribution, and cooling (liquid to chip)
  • Practical familiarity with NVIDIA hardware (such as GPUs, ETH/IB networking components, storage, etc.) within extensive AI and HPC cluster settings.
  • Background with at scale GPU systems in general, encompassing performance testing, AI benchmarking, and more.

What the JD emphasized

  • large-scale AI and HPC GPU infrastructure
  • customer lifecycle
  • large-scale cluster environments
  • AI and HPC GPU infrastructure
  • AI and HPC cluster settings

Other signals

  • customer-facing technical role
  • deploying AI/HPC GPU infrastructure
  • customer lifecycle support