Level

NVIDIA

Senior Solutions Architect, Agentic AI - Public Sector

AI in this role

Architect and deliver generative AI, LLM, and agentic workflows while leading model training and optimization for public sector clients.

hugging-facepytorchtensorflownvidia-platformsllmsgpt-3bert
ragfine-tuninggenerative-aiagentic-aimodel-trainingsolution-architecture

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.
  • Collaborating and working with Government, Research and Public sector departments


What we need to see:

  • B.Tech ,Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
  • 10+ years of hands-on experience in a technical 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:

  • 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.

How we rate this

Senior Solutions Architect, Agentic AI - Public Sector at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGFine TuningGenerative AIAgentic AIModel TrainingSolution ArchitectureHugging FacePyTorch

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. Tell me about a project where generative ai was part of your work. What did you do?
  4. Tell me about a project where agentic ai was part of your work. What did you do?
  5. Tell me about a project where model training was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, Fine Tuning, Generative AI, Agentic AI, and Model Training. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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