NVIDIA
Technology
SeniorSolutionsArchitect-GenerativeAI
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“Senior Solutions Architect - Generative AI at NVIDIA. Skills: Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LLM training, LLM deployment, Agentic AI workflows. Architect end-to-end generative AI solutions with a focus on LLMs training, deployment and Agentic AI workflows. Collaborate closely with customers to understand their language-related business challenges and design tailored solutions”
Industry & Context.
What They're Looking For.
Must Have
5+ years of hands-on experience in a technical AI role, specifically focusing on generative AI, with a 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, Nemotron, 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, 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
Nice to Have
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
What You'll Do.
Architect end-to-end generative AI solutions with a focus on LLMs training
deployment and Agentic AI workflows
Collaborate closely with customers to understand their language-related business challenges and design tailored solutions
Collaborate with sales and business development teams to support pre-sales activities
including technical presentations and demonstrations of LLM and Agentic AI capabilities
Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI software
Engage directly with customers/partners to understand their requirements and challenges
Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and Agentic AI workflows
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 AI Agents 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 training LLMs and implementing RAG-based solutions
How You'll Work.
Team & Collaboration
Collaborate closely with customers; Collaborate with sales and business development teams; Work closely with NVIDIA engineering teams; Engage directly with customers/partners; Excellent communication and collaboration skills
Communication Scope
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
Full Job Description
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 with a focus on LLMs training , deployment and Agentic AI workflows. * Collaborate closely with customers to understand their language-related business challenges and design tailored solutions. * Collaborate with sales and business development teams to support pre-sales activities, including technical presentations and demonstrations of LLM and Agentic AI capabilities. * Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI software. * Engage directly with customers/partners to understand their requirements and challenges. * Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and Agentic AI 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 AI Agents 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 so
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