Senior Solutions Architect

4 weeks ago

Bengaluru, Karnataka, India NVIDIA Full time

NVIDIA is seeking a highly skilled Generative AI Solutions Expert to join our AI Solutions team. As a key member of this team, you will play a pivotal role in architecting and delivering cutting-edge solutions that leverage the power of NVIDIA's generative AI technologies.

Key Responsibilities:

  • Architect end-to-end generative AI solutions with a focus on Large Language Models (LLMs) training, deployment, and Retrieval-Augmented Generation (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 software.
  • Engage directly with customers/partners to understand their requirements and challenges.
  • Lead workshops and design sessions to define and refine generative AI solutions focused on LLMs and RAG workflows.
  • 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.
  • Provide technical leadership and guidance on best practices for training LLMs and implementing RAG-based solutions.

Requirements:

  • Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
  • 7-11+ years of hands-on experience in a technical AI 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.

Preferred Qualifications:

  • 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.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. We value diversity in our current and future employees and do not discriminate 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.

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