Software Solutions Architect - Nvis

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1 · Remote

This role focuses on helping customers deploy and integrate NVIDIA's AI and machine learning software stacks into their existing ML ops environments. The individual will collaborate with various stakeholders, diagnose issues, and develop scripts to streamline deployment and improve model performance, primarily within cloud-native environments using Kubernetes.

What you'd actually do

  1. Helping our customers deploy cloud-native software stacks, such as NVIDIA AI, to embed guidelines in machine learning operations.
  2. Collaborating closely with infrastructure admins, data scientists, and ML engineers to deploy tools like NVIDIA Run:ai and NVIDIA Mission Control.
  3. Integrating NVIDIA software into customers’ existing ML ops environments and developing scripts to streamline these processes.
  4. Diagnosing and resolving customer issues within sophisticated cloud-native environments, applying profiling and analysis tools to improve deep learning model performance.
  5. Working with internal collaborators, including Pre-Sales Solution Engineers, Account Managers, and Product teams, to understand business requirements and provide technical insights.

Skills

Required

  • 5 years of experience in a customer-facing role
  • B.Sc in Computer Science or equivalent experience
  • Proven experience presenting to technical audiences and crafting technical content for developers
  • Proficiency in Linux/Unix Operating Systems
  • Hands-on experience with Kubernetes and containerization technologies like Docker
  • Demonstrated experience in software development
  • Certification as a Kubernetes Administrator (CKA)
  • Experience with AI and ML tools

Nice to have

  • NVIDIA Run:ai
  • NVIDIA Mission Control

What the JD emphasized

  • customer-facing role
  • AI and ML tools

Other signals

  • customer-facing role
  • deploying AI/ML solutions
  • integrating NVIDIA software
  • streamlining processes
  • improving deep learning model performance