We are building the world's leading AI company and are looking for an experienced Cloud Solution Architect to help assist customers with adoption of GPU hardware and Software, as well as building and deploying Machine Learning (ML), Deep Learning (DL), data analytics solutions on various Cloud Computing Platforms.
As part of the Solutions Architecture team, we work with some of the most exciting computing hardware and software technologies including the latest breakthroughs in machine learning and data science. A Solutions Architect is the first line of technical expertise between NVIDIA and our customers so you will engage directly with developers, researchers, and data scientists with some of NVIDIA's most strategic technology customers as well as work directly with business and engineering teams on product strategy.
What you will be doing:
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Working with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA's ML/DL and data science software and hardware technologies
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Build and deploy AI/ML solutions at scale using NVIDIA's AI software on cloud-based GPU platforms.
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Build custom PoCs for solution that address customer's critical business needs applying NVIDIA hardware and software technology
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Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions for ML/DL and other software solutions
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Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.
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Conduct regular technical customer meetings for project/product roadmap, feature discussions, and intro to new technologies. Establish close technical ties to the customer to facilitate rapid resolution of customer issues
What we need to see:
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3+ years of Solutions Engineering (or similar Sales Engineering roles) or equivalent experience
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3+ years of work-related experience in Deep Learning and Machine Learning, including deep learning frameworks TensorFlow or PyTorch, GPU, and CUDA experience extremely helpful.
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BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or other Engineering fields or equivalent experience.
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Established track record of deploying solutions in cloud computing environments including AWS, GCP, or Azure
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Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, data center deployments
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Ability to use at least one scripting language (i.e., Python)
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Good programming and debugging skills
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Ability to communicate your ideas/code clearly through documents, presentation etc.
Ways to stand out from the crowd:
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AWS, GCP or Azure Professional Solution Architect Certification.
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Hands-on experience with NVIDIA GPUs and SDKs (e.g. CUDA, RAPIDS, Triton etc.)
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System-level experience specifically GPU-based systems
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Experience with Deep Learning at scale
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Familiarity with parallel programming and distributed computing platforms
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