Senior Solutions Architect, Generative AI
NVIDIA is hiring a Senior Solutions Architect, Generative AI in Mumbai, India. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Works on AI ●●●○ is how Level rates this solutions architect role: the daily work is on AI products, without building the model.
- The posting mentions Hugging Face, PyTorch and TensorFlow among its tools and skills.
- Level classes it as a senior role.
- NVIDIA has 2,700+ other open roles on Level.
AI in this role
Architect and deliver enterprise generative AI solutions focusing on LLM training and RAG workflows.
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 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 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 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:
Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
5+ 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:
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
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, Generative AI at NVIDIA rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical solutions architect job
- $169k
This posting doesn't list pay. Use the typical solutions architect figure when pay comes up. See solutions architect jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a project where generative ai was part of your work. What did you do?
- Tell me about a project where llm was part of your work. What did you do?
- Tell me about a project where solutions architecture was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, Fine-tuning, Generative AI, LLM, and Solutions Architecture. 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.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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