Solutions Architect, CSP GTM

NVIDIA's Worldwide Field Operations (WWFO) team is looking for a Solution Architect with expertise in Generative AI, Data Science applications and Machine Learning (ML) to work with our Hyperscaler partners.

As a Solutions Architect, you will be the first line of technical expertise between NVIDIA, our Hyperscaler partners and our end-customers. You will be working with the latest NVIDIA technologies coupled with the most advanced CSP infrastructures, changing the way people interact with technology.

Responsibilities:

• Develop and demonstrate solutions based on Hyperscalers and NVIDIA's pioneering GenAI software and hardware technologies to developers
• Work directly with key customers and our Hyperscalers partners to understand their challenges and provide the best solutions based on NVIDIA products
• Perform in-depth analysis and optimization to ensure the best performance on GPU-accelerated systems using NVIDIA software platform
• Partner with Engineering, Product and Sales teams to understand developer's challenges and plan for the best suitable solutions
• Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures

Requirements:

• 5+ years of Solutions Architect/Engineering or similar experience in AI-related fields
• Excellent ability to listen, both verbal and written communication skills, and being comfortable with presenting technical solutions in English
• Expertise in deploying large-scale training and inferencing pipeline on Hyperscaler's infrastructure, with a focus on GCP
• MS/PhD or equivalent in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields
• A proven track record of academic and/or industry experience in fields related to machine learning, deep learning and/or data science

Nice to Have:

• Background with Hyperscaler platforms such as GCP
• Experience running and optimizing large scale distributed DL training
• Experience optimizing inference pipeline, using a range of inferencing technics
• Background with working with larger transformer-based architectures
• Experience using DevOps technologies such as Docker, Kubernetes, Singularity, etc.