Senior Solutions Architect, Generative AI Deployment and AIOps

NVIDIA is seeking outstanding AI Solutions Architects to assist and support customers that are building solutions with our newest AI technology.

As a Senior Solutions Architect, Generative AI Deployment and AIOps, you will become a trusted technical advisor with our customers and work on exciting projects and proof-of-concepts focused on inference for Generative AI and Large Language Models (LLMs).

Responsibilities:

• Partner with solution architects, engineering, product, and business teams to understand their strategies and technical needs and help define high-value solutions
• Engage with developers, scientific researchers, and data scientists to gain experience across a range of technical areas
• Collaborate with lighthouse customers and industry-specific solution partners targeting our computing platform
• Work closely with customers to help them adopt and build creative solutions using NVIDIA technology and MLOps solutions
• Analyze performance and power efficiency of AI inference workloads on Kubernetes
• Travel to conferences and customers may be required (20%)

Requirements:

• BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
• 8+ years of hands-on experience with Deep Learning frameworks such as PyTorch and TensorFlow
• Strong fundamentals in programming, optimizations, and software design, especially in Python
• Proficiency in problem-solving and debugging skills in GPU orchestration and Multi-Instance GPU (MIG) management within Kubernetes environments
• Experience with containerization and orchestration technologies, monitoring, and observability solutions for AI deployments
• Excellent knowledge of the theory and practice of LLM and DL inference
• Excellent presentation, communication, and collaboration skills

Preferred Qualifications:

• Prior experience with DL training at scale, deploying or optimizing DL inference in production
• Experience with NVIDIA GPUs and software libraries such as NVIDIA NIM, Dynamo, TensorRT, TensorRT-LLM
• Excellent C/C++ programming skills, including debugging, profiling, code optimization, performance analysis, and test design
• Familiarity with parallel programming and distributed computing platforms