NVIDIA
SolutionsArchitect-GenAI
Neural analysis suggests this role is
optimal for Senior candidates.
“Solutions Architect - Gen AI at NVIDIA. Skills: Generative AI, Large Language Models (LLMs), Agentic AI, RAG-based workflows, NVIDIA's generative AI technologies. Architect end-to-end generative AI solutions with a focus on LLMs, Agentic and RAG workflows. Collaborate closely with customers to understand their language-related business challenges and design tailored solutions”
What You'll Achieve.
architecting and delivering cutting-edge solutions; achieve optimal performance
Industry & Context.
understand their language-related business challenges and design tailored solutions; define and refine generative AI solutions
What They're Looking For.
Must Have
B. Tech, Master's or Ph. D. in Computer Science, Artificial Intelligence, or equivalent experience, 8+ years of hands-on experience in a technical 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-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, 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
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
Agentic and RAG 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 RAG capabilities
Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI technologies
Engage directly with customers to understand their language-related requirements and challenges
Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and RAG 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 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 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; articulate complex technical concepts to both technical and non-technical stakeholders
Communication Scope
Excellent communication and collaboration skills; ability to articulate complex technical concepts to both technical and non-technical stakeholders; 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 Agentic AI . 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 LLMs, and a strong proficiency in designing and implementing agentic and RAG-based workflows. **What you will be doing:** * Architect end-to-end generative AI solutions with a focus on LLMs, Agentic and RAG 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 RAG capabilities. * Work closely with NVIDIA engineering teams to provide feedback and contribute to the evolution of generative AI technologies. * Engage directly with customers to understand their language-related requirements and challenges. * Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and RAG 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 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:** * B.Tech ,Master's or Ph.D. in Computer Science, Artificial Intelligence
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