NIM Solution Architect

Designs and deploys NVIDIA's inference microservices (NIM) for enterprise customers, optimizing LLM and VLM workloads across on-premises and cloud environments. Balances hands-on model tuning and infrastructure work with customer guidance and cross-functional collaboration. Unusual for a Solutions Architect role: deep technical depth in inference optimization, PyTorch, and RL rollout rather than pure architecture and design.

NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. SA team is more focusing to bring NVIDIA new technology into difference industries. This role focuses on NVIDIA Inference Microservices (NIM), inference / RL rolloutperformance, and AI workflow enablement for LLM, VLM, and other generative AI workloads. It is a highly hands-on position at the intersection of model optimization, inference infrastructure, and customer solution delivery.  What you’ll be doing: Drive the implementation, deployment, and optimization of NVIDIA Inference Microservices (NIM) solutions for enterprise and industry AI workloads. Package and serve open-source, NVIDIA, and customer-proprietary models through NIM with standardized, containerized APIs for on-premises, cloud, and hybrid environments. Optimize high-volume inference and rollout workloads for LLMs and VLMs. Evaluate and tune the NIM models. Deliver technical projects, demos and client support tasks as directed by the Solution Architecture Leadership. Provide technical support and guidance to customers, facilitating the adoption and implementation of NVIDIA technologies and products. Collaborate with cross-functional teams to enhance and expand our AI solutions portfolio. What we need to see: Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related technical field, or equivalent experience. 2+ years of hands-on experience in machine learning engineering, applied research, LLM/VLM inference, or RL rollout. Production-quality Python and PyTorch skills, including distributed GPU training, solution, profiling, debugging, memory optimization. Working knowledge of transformer architectures, performance optimization, rollout sampling strategies, structured generation, and model-quality evaluation. Strong written and verbal communication skills, with the ability to collaborate effectively across research, engineering, infrastructure, product, and customer-facing teams. Ways to stand out from the crowd: Publications, open-source contributions, or significant technical projects, LLM/VLM, agent systems. Experience applying programmatic verification, simulators, compilers, execution sandboxes, APIs, or external tools as reward sources for model training. agent system. Familiar with oss RL framework such as SLIME, Nemo-RL. Familiarity with enterprise AI deployment, customer adaptation, or adapting foundation models to specialized vertical domains.