Solutions Architect - NVIDIA Cloud Partners and Datacentre Infrastructure
RemoteUnited Arab Emirates
Job Summary
Design, implement, and operationalize NVIDIA's cutting-edge hardware and software solutions for partners, ensuring seamless integration with datacentre power, cooling, and MEP systems. Serve as the primary technical contact for customers throughout the development, construction, and production of GPU cloud infrastructure, guiding them on power distribution, cooling strategies, and MEP integration. Collaborate with sales leads to identify business opportunities, conduct technical meetings for debugging and product introductions, and build Proofs of Concept for AI and HPC workloads. Prepare presentations on datacentre best practices and analyze joint solutions to address performance bottlenecks and scaling issues. Advise customers on modular construction, efficient power management, and advanced cooling techniques while supporting compliance with electrical and environmental standards. Provide feedback to engineering teams to shape future product and infrastructure strategy.
Required Qualifications
- BS/MS/PhD in Mechanical/Electrical Engineering, or other Engineering fields or equivalent experience
- 12+ years in Solution Engineering (or similar Sales Engineering, Cloud Engineering) working directly with partners and customers
- Motivation and skills to own and drive technical engagements with customers throughout full customer life-cycle
- At least one of the following datacenter certifications ATD, CDCAP, CDCDP, CDCEP, CDCMP, CDCSP is requested
- 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.
Desired Qualifications
- 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
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