Deep Learning Solution Architect - Agentic Performance

NVIDIA are seeking dynamic Solution Architects with specialized expertise in training Large Language Models (LLMs), implementing RAG workflows, and agentic inference.

You will leverage the full NVIDIA software & hardware ecosystem to design, optimize, and deliver production-grade generative AI solutions for enterprise customers.

What You Will Be Doing:

• Architect end-to-end solutions focused on LLM pretraining, fine-tuning, high-performance inference, RAG workflows, and agentic inference orchestration using NVIDIA’s hardware and software platforms.
• Collaborate with customers to understand their LLM-related business challenges and design tailored solutions aligned with the NVIDIA ecosystem.
• Lead LLM training, distributed optimization, and performance tuning to achieve optimal throughput, latency, and memory efficiency.
• Design and integrate RAG workflows and agentic inference pipelines into customer systems; provide technical guidance on best practices.
• Collaborate with NVIDIA engineering teams to provide feedback and support pre-sales technical activities (workshops, demos).

What We Need to See:

• Master’s / Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience.
• 4+ years hands-on experience in AI, focusing on open-source LLM training, fine-tuning, and production inference optimization.
• Deep understanding of mainstream LLM architectures and proficiency in LLM customization via PyTorch, Hugging Face Transformers.
• Solid knowledge of GPU computing, cluster architecture, and distributed parallel training/inference for LLMs.
• Competency in agentic inference design and using AI agents to solve business challenges.
• Strong communication skills, able to articulate complex technical concepts to technical and non-technical stakeholders.

Ways to Stand Out from the Crowd:

• Hands-on experience with NVIDIA’s generative AI ecosystem (TRT-LLM, Megatron-LM, NVIDIA NeMo).
• Advanced skills in LLM optimization (quantization, KV Cache tuning, memory footprint reduction).
• Experience with Docker, Kubernetes for containerized LLM and agent workflow deployment on-prem.
• In-depth knowledge of multi-GPU parallelism and large-scale GPU cluster management.