Software Qa Tools Development Engineer

NVIDIA NVIDIA · Semiconductors · Pune, India

NVIDIA is looking for a Tools Development Engineer to join their Certification team. The role involves ramping up on existing automation tools, building new solutions to improve productivity, and integrating AI/ML and agentic AI solutions to optimize workflows. The engineer will also work with AI-native development tools and understand LLM failure modes.

What you'd actually do

  1. Utilize various certification kits to test software and hardware features of our products, ensuring certification-level quality across all stages of the development cycle, from emulation to production. This includes a wide range of functional areas such as graphics, display, audio, multimedia, USB, hypervisors, and virtual machines.
  2. Develop a thorough understanding of all functional and process-related areas within the hardware and functional certification ecosystem.
  3. Work actively with existing UI and automation tools by integrating internal and external APIs, formulate and deploy multi-agent systems to optimize existing workflows and to build new automated solutions.
  4. Communicate accurate, timely, and effective status updates to management and key internal and external partners.
  5. Drive operational excellence by defining and improving processes, using innovative problem-solving methods to increase organizational efficiency and support strategic goals.

Skills

Required

  • Python
  • C#
  • AI/ML
  • agentic AI solutions
  • AI-native development tools
  • LLM failure modes
  • .NET Core
  • SQL
  • GitHub
  • Perforce
  • Linux environments
  • advanced scripting
  • command-line operations

Nice to have

  • Google xTS
  • HLK
  • HDMI
  • DP
  • ESXi
  • Codex
  • Claude Code
  • Cursor
  • LLM APIs
  • Windows
  • Android

What the JD emphasized

  • excellent programming skills in Python and C#
  • Demonstrated ability to design, build, and deploy AI/ML and agentic AI solutions
  • Strong familiarity with AI-native development tools
  • Clear understanding of LLM failure modes

Other signals

  • AI/ML solutions to accelerate automation development
  • multi-agent systems to optimize existing workflows
  • LLM failure modes