Nvidia AI Enterprise - AIML Solution Engineer/Architect

Preferred Skills:

NVIDIA GPU setup and configuration on ESXi/Hyper-V/KVM, GPU Clustering, CUDA, NVIDIA AI Enterprise Suite (Nemo, Triton, GPU and Network Operator, Kubernetes, Docket, Machine Learning, Training and Inference, RAG, LLM, PyTorch, Python. AWS, GCP

Roles and Responsibilities:

• Architect end-to-end generative AI solutions with a specialized focus on Large Language Models (LLMs) training, deployment, and Retrieval-Augmented Generation (RAG) workflows, leveraging NVIDIA's AI Enterprise technologies.
• Collaborate closely with sales and business development teams to drive pre-sales engagements, including conducting proof-of-concept (PoC) development, delivering technical presentations, and demonstrating LLM and RAG capabilities within NVIDIA AI Enterprise Suite.
• Design, deploy, and optimize AI solutions powered by NVIDIA GPUs across diverse environments. Lead workshops and design sessions that define and advance generative AI solutions, and spearhead the training, fine-tuning, and optimization of Large Language Models using NVIDIA hardware and software platforms.
• Demonstrate deep expertise in Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise Suite, NVIDIA Inference Microservices (NIMs), model management, Kubernetes orchestration, and AI/ML frameworks such as PyTorch and TensorFlow.
• Possess thorough knowledge of GPU cluster architectures, parallel and distributed computing principles, and hands-on experience managing NVIDIA GPU technologies and clusters to build scalable and performant workflows for LLM training and inference.
• Deploy AI solutions proficiently across enterprise platforms, including VMware ESXi, Docker, Kubernetes, Linux (Ubuntu/RHEL), and Windows Server environments, with practical MLOps experience utilizing tools like Kubeflow, MLflow, or AWS SageMaker to manage AI model lifecycles in production.
• Configure, install, and validate AI infrastructure on-premises and in cloud environments, ensuring optimal GPU-accelerated workload performance using frameworks such as PyTorch and TensorFlow.
• Maintain in-depth understanding of state-of-the-art language models and expertise in training and fine-tuning LLMs with popular frameworks, optimizing models for inference speed, memory efficiency, resource utilization, and production-level accuracy and scalability.
• Collaborate effectively with cross-functional teams, customers, and internal stakeholders to coordinate deployments, troubleshoot technical challenges, and ensure robust AI solution integration with enterprise workflows.
• Execute containerization and orchestration of AI workloads using Docker and Kubernetes, and possess experience deploying LLM models both in leading cloud environments (AWS, Azure, GCP) and on-premises infrastructure.