NVAIE Technical Solutions Architect

4 weeks ago

Shanghai, Shanghai, China NVIDIA Full time

NVIDIA stands at the forefront of AI computing innovation. Our dedicated team is enthusiastic about AI, High-Performance Computing (HPC), visual technologies, and gaming. The role of the Solution Architect (SA) team is pivotal in deploying NVIDIA's cutting-edge technology across diverse sectors. We are instrumental in crafting AI computing frameworks and evaluating AI and HPC applications to deliver significant value to our clientele.

In this role, you will work in close collaboration with NVAIE teams, headquarters NIM teams, and fellow SAs to provide comprehensive NVIDIA solutions tailored to our customers' needs.

NVAIE Technical Solutions Architect

Key Responsibilities:

  • Assist in enhancing the NVAIE team's capabilities and fostering business growth in the region.
  • Provide support to leading NVAIE partners and LHA on Proof of Concepts (POCs), solution architecture, and various technical elements.
  • Implement technical initiatives, demonstrations, and client support activities as directed by the Solution Architecture leadership.
  • Offer technical guidance for NVAIE alongside GPU/Network systems.
  • Establish yourself as an authority in the integration of NVIDIA technology, particularly NVAIE, across multiple industries.
  • Serve as an internal proponent for NVIDIA software and holistic solutions within the technical ecosystem.

Qualifications:

  • Minimum of 3 years of experience in job scheduling, resource management, cluster management, and MLOps.
  • Outstanding verbal and written communication abilities.
  • Capability to operate autonomously with minimal oversight.
  • Proficiency in programming languages such as C/C++, Python, Java, and Scala.
  • Strong enthusiasm for engaging with a variety of innovative projects.
  • Familiarity with scale-out cloud environments, job scheduling (Kubernetes, Slurm, grid engine, PBS, etc.), container orchestration, and parallel programming techniques.
  • Advanced degree (MS or PhD) in Engineering, Mathematics, Physics, Computer Science, Data Science, or equivalent professional experience.

Preferred Qualifications:

  • Experience with cluster resource management solutions.
  • Expertise in Deep Learning frameworks and associated tools.
  • Understanding of CUDA optimization techniques.
  • Extensive experience in designing and executing large-scale HPC and enterprise computing infrastructures.

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