Nvidia Solutions Architect - Financial Service Retail 

China, Beijing, Beijing 

342837628

What You’ll Be Doing:

• Conduct in-depth analysis of customers' latest needs and co-develop accelerated computing solutions with key customers.

• Assist in supporting industry accounts and drivingresearch/influencing/newbusiness in those accounts.

• Deliver technical projects, demos and client support tasks as directed by the Solution Architecture leadership team.

• Understand and analyze customers' workloads and demands for accelerated computing, including but not limited to: LLM training/inference acceleration and optimization, application optimization for Agent AI/RAG, kernel analysis, etc.

• Assist customers in onboarding NVIDIA's software and hardware products and solutions, including but not limited to: CUDA, TensorRT-LLM,NeMoFramework, etc.

• Be an industry thought leader on integrating NVIDIA technology into applications built on Deep Learning, High Performance Data Analytics, Robotics, Signal Processing and other key applications.

• Be an internal champion for Data Analytics, Machine Learning, and Cyber among the NVIDIA technical community.

What We Need To See:

• 3+ years’ experience withresearch/development/applicationof Machine Learning, data analytics, or computer vision work flows.

• Outstanding verbal and written communication skills

• Ability to work independently with minimal day-to-day direction

• Knowledge of industry application hotspots and trends in AI and large models.

• Familiarity with large model-related technology stacks and common inference/training optimization methods.C/C++/Python programming experience

• Desire to be involved in multiple diverse and innovative projects

• Experience using scale-out cloud and/or HPC architectures for parallel programming

• MS or PhD in Engineering, Mathematics, Physics, Computer Science, Data Science, Neuroscience, Experimental Psychology or equivalent experience.

Ways To Stand Out From The Crowd:

• AIGC/LLM/NLP experience

• CUDA optimization experience.

• Experience with Deep Learning frameworks and tools.

• Engineering experience in areas such as model acceleration and kernel optimization.

• Extensive experience designing and deploying large scale HPC and enterprise computing systems.