Senior Solution Architect, Generative AI - CSP

Job description

NVIDIA’s Solution Architect team is looking for a AI-focused Solution Architect with expertise in Large Language Model, generative AI, agentic AI. We work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence. We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone always thinking about artificial intelligence, someone who can maintain constructive collaboration in a fast paced, rapidly evolving field, someone able to coordinate efforts between corporate marketing, industry business development and engineering. You will be working with the latest AI architecture coupled with the most advanced neural network models, changing the way people interact with technology. What You Will Be Doing: Architect end-to-end generative AI solutions with a focus on LLMs training , deployment and RAG workflows. Collaborate closely with customers to understand their language-related business challenges and design tailored solutions. Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI software. Engage directly with customers/partners to understand their requirements and challenges. Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and RAG workflows and lead the training and optimization of Large Language Models using NVIDIA’s hardware and software platforms. Implement strategies for efficient and effective training of LLMs to achieve optimal performance. Provide technical leadership and guidance on best practices for training LLMs and implementing RAG-based solutions. What We Need To See: Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience 7+ years of hands-on experience in a technical AI role, specifically focusing on generative AI, with a strong emphasis on training Large Language Models (LLMs). Proven track record of successfully deploying and optimizing LLM models for inference in production environments. Expertise in training and fine-tuning LLMs using popular frameworks such as Megatron-LM, Megatron-Bridge, AutoModel and PyTorch. Proficiency in model deployment and optimization techniques for efficient inference on various hardware platforms, with a focus on GPUs. Strong knowledge of GPU cluster architecture and the ability to leverage parallel processing for accelerated model training and inference. Excellent communication and collaboration skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders. Experience leading workshops, training sessions, and presenting technical solutions to diverse audiences. Ways To Stand Out From The Crowd: Experience in deploying LLM models in cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure. Proven ability to optimize LLM models for inference speed, memory efficiency, and resource utilization. Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment. 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