Nvidia Solutions Architect Generative AI 

United States, Texas 

168080548

What you’ll be doing:

• Develop and demonstrate software solutions based on NVIDIA’s ground breaking AI software and hardware technologies to customers. Develop GenAI model pipeline and perform in-depth analysis and optimization to ensure the best performance on current- and next-generation GPU architectures

• Develop and debug software for NVIDIA and OSS AI frameworks and libraries

• Lead and develop proof-of-concepts (PoCs) for software solutions applied to Consumer Internet industry use-cases such as NLP/LLM, retrieval, recommender, etc. by working closely with customer's AI developers. Build collateral (notebook/code) for PoCs

• Work closely with business development team owning the technical relationship and enabling customer in building innovative solutions based on NVIDIA technologies

• Partner with NVIDIA software engineering, product, sales teams to secure design wins at customers. Enable development and growth of NVIDIA product features through customer feedback and PoC evaluations

What we need to see:

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

• 5+ years of experience as an AI/Software Engineer with proven track record coding in Python and/or C++ with popular AI software libraries and GPUs

• Experience with GenAI applications and LLMtraining/fine-tuning,inference optimization and/or RAG pipelines

• Ability to communicate your ideas/code clearly through GitHub, documentation

• Great teammate who enjoys collaborating with teams across the organization such asEngineering/Research,Sales, Product, and Marketing

• Effective verbal/written communication, and technical presentation skills

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

Ways to stand out from the crowd:

• Experience working with enterprise developers and customer facing skills

• Experience with large-scale production data pipelines and AI model training/deployment

• Knowledge of MLOps technologies such as containers, Kubernetes, data center deployments etc.

• Able to think creatively to debug and solve complex problems