Nvidia Solutions Architect AI ML 

United States, Texas 

618441158

What you will be doing:

• Working with Cloud Service Providers to develop and demonstrate solutions based on NVIDIA’s ML/DL and data science software and hardware technologies

• Build and deploy AI/ML solutions at scale using NVIDIA's AI software on cloud-based GPU platforms.

• Build custom PoCs for solution that address customer’s critical business needs applying NVIDIA hardware and software technology

• 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

• Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.

• 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:

• 3+ years of Solutions Engineering (or similar Sales Engineering roles) or equivalent experience

• 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.

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or other Engineering fields or equivalent experience.

• Established track record of deploying solutions in cloud computing environments including AWS, GCP, or Azure

• Knowledge of DevOps/ML Ops technologies such as Docker/containers, Kubernetes, data center deployments

• Ability to use at least one scripting language (i.e., Python)

• Good programming and debugging skills

• Ability to communicate your ideas/code clearly through documents, presentation etc.

Ways to stand out from the crowd:

• AWS, GCP or Azure Professional Solution Architect Certification.

• Hands-on experience with NVIDIA GPUs and SDKs (e.g. CUDA, RAPIDS, Triton etc.)

• System-level experience specifically GPU-based systems

• Experience with Deep Learning at scale

• Familiarity with parallel programming and distributed computing platforms