
Solutions Architect - Gen AI
NVIDIA · Bengaluru, Karnataka, India
- On site
- Full-time
- $150,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Architect advanced generative AI solutions with LLMs.
- Collaborate with customers on business challenges.
- Support sales with technical presentations.
- Train and optimize LLMs using NVIDIA hardware.
- Design and implement RAG-based workflows.
About the role
Solutions Architect - Generative AI
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, or equivalent experience
- 8+ 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. JR2010605
Key skills/competency
- Generative AI
- Large Language Models (LLMs)
- Agentic AI
- RAG Workflows
- NVIDIA Technologies
- Model Training
- Model Optimization
- GPU Computing
- Solution Architecture
- Technical Leadership
Skills & topics
- Solutions Architect
- Generative AI
- Large Language Models
- LLM
- Agentic AI
- RAG
- NVIDIA
- GPU
- AI
- Machine Learning
- Deep Learning
- Computer Science
- Artificial Intelligence
- Model Training
- Model Optimization
- Technical Sales
- Pre-sales
- Solution Design
- TensorFlow
- PyTorch
- Hugging Face
- Docker
- Kubernetes
- Cloud Computing
- High-Performance Computing
- HPC
How to get hired
- Tailor your resume: Highlight your 8+ years of generative AI and LLM training experience, showcasing specific achievements with TensorFlow, PyTorch, or Hugging Face.
- Showcase deployment expertise: Emphasize your proven track record in deploying and optimizing LLMs for production inference, especially on GPUs.
- Demonstrate technical breadth: Detail your understanding of GPU cluster architecture, parallel processing, and NVIDIA GPU technologies.
- Prepare for technical interviews: Be ready to discuss complex AI concepts, model training strategies, and RAG workflow implementations.
- Highlight collaboration skills: Prepare examples of leading workshops and presenting technical solutions to diverse audiences.
Technical preparation
Behavioral questions
Frequently asked questions
- What specific NVIDIA hardware and software platforms are used for LLM training in this Solutions Architect role?
- While the description emphasizes using NVIDIA's hardware and software platforms for LLM training, specific details are usually discussed during the interview process. Candidates are expected to have strong knowledge of GPU cluster architecture and parallel processing, suggesting utilization of NVIDIA's high-performance computing solutions like DGX systems and CUDA libraries.
- How does NVIDIA's commitment to diversity impact the hiring process for a Solutions Architect - Generative AI?
- NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. This means they value diversity in their employees and do not discriminate based on various protected characteristics. The hiring process aims to be inclusive, focusing on skills and experience relevant to the Solutions Architect role.
- What is the expected career progression for a Solutions Architect - Generative AI at NVIDIA?
- As a Solutions Architect at NVIDIA, career progression could involve moving into more senior architectural roles, leading larger teams, specializing further in specific AI domains, or transitioning into product management or strategic technical leadership positions within the company's rapidly growing AI divisions.
- Can a candidate with extensive experience in other AI models, but not specifically LLMs or Agentic AI, be considered for this NVIDIA role?
- The job description heavily emphasizes specialized expertise in LLMs, Agentic AI, and RAG workflows. While a strong AI background is valuable, candidates without direct, deep experience in these specific areas might find it challenging to meet the core requirements. However, showcasing transferable skills in model training, optimization, and deployment on GPUs could be beneficial.
- What kind of customer engagement can I expect as a Solutions Architect - Generative AI at NVIDIA?
- As a Solutions Architect, you will engage directly with customers to understand their language-related business challenges, lead workshops, conduct design sessions, and present technical solutions. This involves collaborating closely with them to integrate and optimize generative AI solutions, particularly LLMs and RAG workflows, into their applications and systems.