Description
We're seeking an experienced AI Infrastructure Solutions Architect to bridge design to deployment of large-scale GPU infrastructure. As part of the NVIDIA SA organisation, you will interact with customers, partners, and internal teams to analyse, define, and implement large-scale AI/HPC projects. Your responsibilities will include working with NVIDIA Cloud Partners in Canada on large data centre GPU server and networking system deployments, guiding customer discussions on network design, compute/storage, and support bring up of server/network/cluster deployments. You will need to visit customer data centres during the bring-up phase.
You will become the primary technical driver for customers during the design, development, construction, integration, and production of GPU Cloud infrastructure and applications throughout the entire customer lifecycle. This includes working as the customer's trusted advisor conducting regular technical customer meetings for product roadmap, cluster issue debugging, feature discussions, and introduction to new technology solutions.
You will partner with other SAs, Account Managers, Engineering, Product, and business leaders to align on strategies, assess technical needs, and secure business opportunities for NVIDIA. You will analyse and debug compute/network configuration and performance issues to deliver performant clusters. You will prepare and deliver technical content to customers including presentations, workshops, reference architectures, tutorials, and publications.
We need to see a Bachelor's, Master's, or PhD in Electrical/Computer Engineering, Computer Science, Physics, Mathematics, or other Engineering fields or equivalent experience. Five plus years of Solution Engineering (or similar Sales Engineering, Cloud Engineering, Solution Architecture) including experience working directly with partners and customers. System-level expertise of CPU/GPU server architecture, NICs, Linux, system software, and kernel drivers. Experience with networking switches for Ethernet/Infiniband, and Data Centre infrastructure (power/cooling).
Knowledge of DevOps/MLOps technologies such as Docker/containers, Kubernetes. Efficient time management and capable of balancing multiple tasks. Excellent presentation, communication, and collaboration skills. Self-starter with a passion for growth, continuous learning, and sharing insights.