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.
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.
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.
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
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you!
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
