NVIDIA is seeking a dynamic and experienced Generative AI Solution Architect with specialized expertise in training Large Language Models (LLMs) and implementing workflows based on Retrieval-Augmented Generation (RAG). As a key member of our AI Solutions team, you will play a pivotal role in architecting and delivering cutting-edge solutions that leverage the power of NVIDIA's generative AI technologies. This position requires a deep understanding of language models, particularly LLMs, and a strong proficiency in designing and implementing RAG-based workflows.

What you will be doing

• Architect end-to-end generative AI solutions with a focus on LLMs and RAG workflows.
• Work closely with our customers stakeholders like product, business, engineering and research in defining their product roadmap based on our AI Software & Hardware platforms.
• Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI technologies.
• 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.
• Design and implement RAG-based workflows to enhance content generation and information retrieval.
• Work closely with customers to integrate RAG workflows into their applications and systems and stay abreast of the latest developments in language models and generative AI technologies.
• Provide technical leadership and guidance on best practices for training LLMs and implementing RAG-based solutions.

• Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
• 5+ years of hands-on experience in a technical 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.
• In-depth understanding of state-of-the-art language models, including but not limited to GPT-3, BERT, or similar architectures.
• Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
• 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.

• 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, memory efficiency, and 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