Nvidia Senior Solutions Architect Retail 

United States, California 

718400861

What you’ll be doing:

• You will be part of our North America Retail Solution Architecture Team working to drive NVIDIA technology adoption and secure design wins at key Fortune 100 customers in both Data Center, Edge and Cloud Deployments.

• Driving adoption of NVIDIA's Accelerated Compute Platforms which use key technologies like virtualization, Kubernetes, Docker, and Run:AI with focus on growing our customer's DevOps and MLOps capabilities by using NVIDIA's modern architectures, solutions and blueprints to deploy world-class computing platforms.

• Leading customer proof-of-concepts (PoCs) of next-gen platforms for deploying Retail Industry solutions to key use-cases.

• Support the business development team through the sales process for GPU/Network hardware/software products. Owning the technical relationship and enabling customer in building innovative solutions based on NVIDIA technologies.

• Partnering with NVIDIA Engineering, Product, Sales teams for assistance in developing solutions and providing customer feedback to enable development and growth of NVIDIA's products

What we need to see:

• BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience)

• 8+ years delivering Enterprise Accelerated Computing (HPC, Deep Learning, Machine Learning) computing infrastructure.

• Demonstrate detailed industry knowledge of Retail with background in modern datacenters for enterprise computing architectures– Storage, Compute, and Software stacks.

• Exposure to GPU technology and GenAI software architectures.

• Direct experience architecting, managing, and supporting shared compute infrastructure software

• Experience working with enterprise developers and academic research community supporting computer vision, data analytics, or Deep Learning deployments

• Enjoy collaborating with teams across the organization such asEngineering/Research,Sales, Product, and Marketing

• Effective verbal/written communication, and technical presentation skills

• Self-starter with a passion for growth and an enthusiasm for continuous learning and sharing findings across the team

Ways to stand out from the crowd:

• Experience architecting large-scale edge and compute clusters.

• Specialty skills in deploying large-scale GPU computing clusters to support AI workloads (Machine Learning, Deep Learning - Training and Inference)

• Experience with Containers, Kubernetes, or BCM for workload orchestration

• Knowledge of MLOps technologies such as Run:AI for large-scale deployments

• Able to think creatively to debug and solve complex problems