Senior Solutions Architect - Generative AI

Senior Solutions Architect - Generative AI at NVIDIA

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 Pretraining, Finetuning LLMs & 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 open source LLMs, and a strong proficiency in designing and implementing RAG-based workflows.

What You Will Be Doing

• Architect end-to-end generative AI solutions focusing on LLMs training, deployment, and RAG workflows.
• Collaborate closely with customers to understand business challenges and design tailored solutions.
• Support pre-sales activities, including technical presentations and demonstrations of LLM and RAG capabilities.
• Work with NVIDIA engineering teams, providing feedback for generative AI software evolution.
• Engage directly with customers/partners to understand their requirements and challenges.
• Lead workshops and design sessions to refine generative AI solutions focused on LLMs and RAG.
• Lead the training and optimization of Large Language Models using NVIDIA’s platforms.
• Implement strategies for efficient and effective LLM training 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.
• Stay abreast of the latest developments in language models and generative AI technologies.
• Provide technical leadership and guidance on best practices for LLM training and RAG 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 in generative AI with LLMs.
• Proven track record of successfully deploying and optimizing LLM models for inference in production.
• In-depth understanding of state-of-the-art language models like GPT-3, BERT, or similar.
• Expertise in training and fine-tuning LLMs using TensorFlow, PyTorch, or Hugging Face.
• Proficiency in model deployment and optimization for efficient inference on GPUs.
• Strong knowledge of GPU cluster architecture and parallel processing for accelerated training.
• Excellent communication and collaboration skills for technical and non-technical stakeholders.
• Experience leading workshops, training, and presenting technical solutions to diverse audiences.

Ways To Stand Out From The Crowd

• Experience deploying LLM models in cloud environments (AWS, Azure, GCP) and on-premises.
• Proven ability to optimize LLM models for inference speed, memory, and resource utilization.
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable deployment.
• Deep understanding of GPU cluster architecture, parallel, and distributed computing concepts.
• Hands-on experience with NVIDIA GPU technologies and GPU cluster management.
• Ability to design and implement scalable workflows for LLM training and inference on GPU clusters.

Key skills/competency

• Generative AI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Deep Learning
• PyTorch
• TensorFlow
• Hugging Face
• GPU Architectures
• Cloud Deployment (AWS, Azure, GCP)
• Technical Leadership