Job
Description
As a Generative AI Solution Architect at NVIDIA, you will leverage your expertise in training Large Language Models (LLMs) and implementing workflows based on Pretraining, Finetuning LLMs & Retrieval-Augmented Generation (RAG). Your role will involve architecting cutting-edge solutions that harness the power of NVIDIA's generative AI technologies. You must possess a deep understanding of language models, especially open source LLMs, and excel in designing and implementing RAG-based workflows. Your responsibilities will include collaborating with customers to identify language-related business challenges and tailor solutions, supporting pre-sales activities by delivering technical presentations and demonstrations, engaging with NVIDIA engineering teams to provide feedback, and working directly with customers/partners to understand their requirements and challenges. You will lead workshops and design sessions to define generative AI solutions focused on LLMs and RAG workflows, and train and optimize Large Language Models using NVIDIA's hardware and software platforms. To qualify for this role, you should hold a Master's or Ph.D. in Computer Science, Artificial Intelligence, or have equivalent experience. Additionally, you must have at least 7 years of hands-on experience in a technical AI role, with a strong emphasis on training LLMs and a proven track record of deploying and optimizing LLM models for production environments. Your expertise should cover state-of-the-art language models like GPT-3, BERT, or similar architectures, and you should be proficient in training and fine-tuning LLMs using frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers. Moreover, you should possess proficiency in model deployment and optimization techniques for efficient inference on various hardware platforms, particularly GPUs. Your knowledge of GPU cluster architecture and parallel processing will be crucial for accelerated model training and inference. Strong communication and collaboration skills are essential for articulating complex technical concepts to diverse audiences and leading workshops and training sessions. To further distinguish yourself, experience in deploying LLM models in cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure, optimizing LLM models for inference speed and resource efficiency, familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes), and a deep understanding of GPU cluster architecture and distributed computing concepts will be advantageous. Join NVIDIA, a technology leader offering competitive salaries, a comprehensive benefits package, and a dynamic work environment. If you are a creative engineer passionate about technology and seeking an opportunity to work with some of the brightest minds in the industry, we invite you to apply and be part of our growing engineering teams.,
