About the job
NVIDIA is looking for an experienced infrastructure Solutions Architect. Do you want to be part of a team that brings Artificial Intelligence (AI) hardware and software technologies to production in the field? We are looking for a networking savvy Solutions Architect to join the NVIDIA team focused on supporting accelerated networking for AI, Machine Learning, and HPC. As part of the NVIDIA Solutions Architecture team, you will be driving end-to-end technology solution integration with some of NVIDIA’s most strategic technology customers.
Responsibilities
Working with Cloud Providers and Hyperscalers to develop, build and deploy compute and networking solutions based on NVIDIA groundbreaking AI infrastructure hardware
Work with customers on POCs for AI solutions to address critical business needs
Conduct regular technical customer meetings for intro to new technologies and products, feature discussions, workshops and performance debugging sessions
Provide recommendations to business and engineering teams on product strategy.
Qualifications
Minimum
BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or other Engineering fields or equivalent experience.
8+ years of Solutions Architect or similar technical pre-sales roles.
Strong sense of achievement, customer oriented demeanor, and skills to drive technical pre-sales activities
Practical knowledge of sophisticated networking for AI data centers, including, GPUs, CPUs, InfiniBand and Ethernet fabric topologies, PCIe, host networking and switches.
Experience with end-to-end compute and network performance debugging - hosts, switches, and optics.
Effective time management and capable of balancing multiple tasks
Ability to communicate ideas clearly through documents, presentations, etc.
Preferred
External customer facing skills and background
Hands-on experience with NVIDIA Ethernet and InfiniBand networking hardware, NICs/HCAs, switches, and GPUs
Good understanding of system hardware architecture impact on network performance, including kernel drivers, PCIe devices, NICs, DPUs and GPUs
Large-scale GPU infrastructure deployments
Experience designing and debugging at a rack scale