14 days ago

Solutions Architect - Generative AI

NVIDIA

On Site
Full Time
$150,000
Bengaluru, Karnataka, India
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Job Overview

Job TitleSolutions Architect - Generative AI
Job TypeFull Time
Offered Salary$150,000
LocationBengaluru, Karnataka, India
Map of Bengaluru, Karnataka, India

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Job Description

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

Tags:

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

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How to Get Hired at NVIDIA

  • 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.

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