Deep Learning Solution Architect - EBC

At NVIDIA, our passion is working with the world's most challenging problems in LLM, MLLM, Generative AI, RAGs using our innovative platforms. A Senior Solution Architect brings focus and technical expertise about NVIDIA technological advances to our partners and customers.

What you’ll be doing:

• Assist field business development in guiding the customer through the sales process for GPU Computing products, owning the technical relationship, and assisting customer in building innovative solutions based on NVIDIA technology.

• Be responsible for LAB and EBC. All customer visits and engagements along with organizing workshops and hands on trainings.

• Be an internal champion for Deep Learning or Data Science among the NVIDIA technical community.

What we need to see:

• B.Tech. in Engineering, Mathematics, Physics, or Computer Science, MS desirable. as well as 2+ years of Deep Learning experience

• Experience working with modern Deep Learning software architecture and application

• Customer facing skill-set and background as well as the ability to communicate effectively with customers

• Exposure to BCM, Infrastructure management, SLURM, K8, storage, InfiniBand is must

• Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, Pytorch, or Hugging Face Transformers.

• Experience working with supercomputing and technical computing customers

• Strong teamwork and interpersonal skills; able to multitask in a fast paced environment

• Strong analytical and problem-solving skills

• Ability to balance multiple accounts during implementation of new technology and products into very complex projects

Ways to stand out from the crowd:

• Specialty skills in large scale computing and cluster computing, machine learning, convolution neural networks

• Deep understanding of GPU cluster architecture, parallel computing, and distributed computing concepts.

• Hands-on experience with NVIDIA GPU technologies, and GPU cluster management and ability to design and implement scalable and efficient workflows for LLM training and inference on GPU clusters