NVIDIA is seeking a seasoned Generative AI Solutions Architect with deep expertise in training Large Language Models (LLMs) and Agentic AI. Join our AI Solutions team to architect and deliver advanced solutions utilizing NVIDIA's cutting-edge generative AI technologies. This role demands a comprehensive understanding of language models, particularly LLMs, and a strong ability to design and implement agentic and RAG-based workflows.

This is an opportunity to be at the forefront of AI innovation, working with a world-class team on impactful projects. We are looking for individuals who are passionate about pushing the boundaries of what's possible with AI and who thrive in a collaborative, fast-paced environment.

Architect comprehensive generative AI solutions, focusing on LLMs, Agentic, and RAG workflows. Collaborate closely with clients to understand their language-based business challenges and devise bespoke solutions. Support pre-sales efforts by delivering technical presentations and demonstrations of LLM and RAG capabilities. Provide valuable feedback to NVIDIA engineering teams to drive the advancement of generative AI technologies. Lead workshops and design sessions to refine generative AI solutions, specifically for LLMs and RAG workflows. Train and optimize Large Language Models using NVIDIA's robust hardware and software platforms, ensuring peak performance. Implement efficient strategies for LLM training and design RAG-based workflows for enhanced content generation and information retrieval. Integrate RAG workflows into client applications and systems, staying current with the latest advancements in language models and generative AI.

A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or equivalent practical experience is required. Possess a minimum of 10 years of hands-on experience in a technical capacity, with a significant focus on generative AI and Large Language Model (LLM) training. Demonstrate a proven history of successfully deploying and optimizing LLM models for production inference. Exhibit an in-depth understanding of advanced language models like GPT-3, BERT, or similar architectures. Showcase expertise in training and fine-tuning LLMs using frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers. Proficiency in model deployment and optimization techniques for efficient inference on various hardware, especially GPUs. Strong knowledge of GPU cluster architecture and parallel processing for accelerated AI tasks. Possess excellent communication and collaboration skills, capable of conveying complex technical concepts to diverse audiences. Experience leading workshops, training sessions, and presenting technical solutions effectively.