NIM Solution Architect

NVIDIA

. Drive the implementation, deployment, and optimization of NVIDIA Inference Microservices (NIM) solutions for enterprise and industry AI workloads .

Posted 9/20/2026full-timeShanghai • ChinaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in implementing and optimizing NVIDIA Inference Microservices for AI workloads, with strong capabilities in machine learning engineering and model evaluation. Proficient in Python and PyTorch, with a focus on high-volume inference and deployment in enterprise environments.

Highest-signal resume keywords

Machine Learning EngineeringPython ProgrammingPyTorch FrameworkNVIDIA Inference MicroservicesModel Evaluation

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningLLM InferenceVLM InferenceDistributed GPU TrainingPerformance OptimizationModel-Quality EvaluationProgrammatic VerificationSimulatorsAPIsOpen-Source Contributions

Soft Skills

Strong Communication SkillsCollaboration

Tools & Technologies

NVIDIA TechnologiesOpen-Source RL FrameworksSLIMENemo-RL

Certifications & Qualifications

Master’s Degree in Computer ScienceMaster’s Degree in Machine LearningMaster’s Degree in Electrical EngineeringMaster’s Degree in Mathematics

Industry Keywords

Enterprise AI DeploymentCustomer AdaptationFoundation ModelsTechnical ProjectsClient Support

Tech Stack

Tools & technologies

CloudMicroservicesPythonPyTorch

About the role

Key responsibilities & impact

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 NIM models
Deliver technical projects, demos, and client support tasks as directed by Solution Architecture Leadership
Provide technical support and guidance to customers, facilitating adoption and implementation of NVIDIA technologies and products
Collaborate with cross-functional teams to enhance and expand the AI solutions portfolio

Requirements

What you’ll need

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, and 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
Publications, open-source contributions, or significant technical projects involving LLM/VLM or agent systems
Experience applying programmatic verification, simulators, compilers, execution sandboxes, APIs, or external tools as reward sources for model training
Familiarity with open-source RL frameworks such as SLIME and Nemo-RL
Familiarity with enterprise AI deployment, customer adaptation, or adapting foundation models to specialized vertical domains