About the job
We are looking for an experienced Solutions Architect to help bridge the gap between design and deployment of large-scale AI and HPC GPU infrastructure. As a part of the NVIDIA solutions architecture team, you will be driving end-to-end technology solution integration with some of NVIDIA's most strategic customers as well as offering recommendations for business and engineering teams based on customer feedback on product strategy.
Responsibilities
Collaborating with NVIDIA Cloud Partners to create, implement, and put into operation NVIDIA's innovative hardware and software solutions.
Partner with Sales Account Managers and other business leads to identify and secure business opportunities for NVIDIA products and solutions.
Act as the primary technical support for customers during the development, construction and production of extensive GPU cloud infrastructure through whole customer lifecycle.
Conduct regular technical customer meetings for project/product details, feature discussions, intro to new technologies, and debugging sessions.
Work with customers to build PoCs for solutions to address critical business needs by building out networking and compute infrastructure.
Prepare and deliver technical content to customers including presentations, workshops, etc.
Analyze and develop joint solutions for customer performance and scaling issues.
Qualifications
Minimum
BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.
7+ 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 data center 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.
Preferred
Practical familiarity with NVIDIA hardware (such as GPUs, ETH/IB networking components, storage, etc.) within extensive AI and HPC cluster settings.
Practical knowledge of NVIDIA systems technology such as NCCL, DCGM, UFM, Mission Control, Base Command Manager, etc.
Background with at scale GPU systems in general, encompassing performance testing, AI benchmarking, and more.
Practical involvement in cluster administration and coordination (SLURM, K8s, etc.)