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Strong employer brand and metro location raise competition, but highly specialized LLM expertise limits candidate pool.
Role requires deep LLM, GPU, and distributed training expertise, making cross-industry transferability low.
Explicit 10+ years requirement plus mandatory LLM training, deployment, and GPU cluster expertise increases shortlisting strictness.
Architect and deliver end-to-end generative AI solutions focused on Large Language Models (LLMs), Agentic, and Retrieval-Augmented Generation (RAG) workflows.
Lead training, optimization, and integration of LLMs using NVIDIA’s hardware and software platforms for customer applications.
Collaborate with customers, sales, and engineering teams to tailor solutions, support pre-sales, and contribute to generative AI technology evolution.
10+ years of hands-on experience in generative AI with strong emphasis on training Large Language Models (LLMs).
Degree requirement: B.Tech, Master's, or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
Proven experience deploying and optimizing LLMs for production inference environments, using frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.
Strong knowledge of GPU cluster architecture and proficiency in leveraging GPUs for LLM training and inference.
Experienced in architecting scalable AI solutions involving LLMs, agentic AI, and RAG workflows in complex environments.
Technically proficient in model deployment optimizations including containerization (Docker) and orchestration (Kubernetes) for GPU clusters.
Capable of leading technical workshops and collaborating across customer, sales, and engineering teams with clear communication.