Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
Agentic AI
DevOps @ 7
GPU
LLM
Machine Learning
RAG
SRE @ 7
Security @ 4
ServiceNow
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
For over 25 years, NVIDIA has been at the forefront of transforming computer graphics, PC gaming, and accelerated computing. NVIDIA is leveraging AI to drive the next era of computing, with GPUs powering computers, robots, and autonomous vehicles.
The engineer will own engineering efforts across NVIDIA enterprise systems and partner with IT leadership to transform reactive support into strategic, AI-infused automated resolution systems. The role focuses on preventing problems before they occur while balancing speed, security, and user experience.
Responsibilities
- Design and implement agentic AI workflows using LLM-based agents, tool calling, RAG patterns, and orchestration frameworks.
- Build robust integrations and automation pipelines across ServiceNow, identity management, monitoring platforms, and enterprise SaaS.
- Own the full stack, from infrastructure to user-facing tools.
- Triage and resolve enterprise issues, with a focus on automation and improving mitigation and resolution times.
- Manage and troubleshoot enterprise-scale collaboration, productivity, AI, and infrastructure systems.
- Trace and identify the root causes of complex, multisystem failures.
- Identify patterns in recurring tickets and build automation or self-service solutions.
- Build and maintain runbooks, troubleshooting guides, and knowledge base articles.
- Mentor team members on troubleshooting methodology and systems thinking.
Requirements
- Bachelor's degree or equivalent experience.
- 8+ years of experience in SRE, enterprise support, or DevOps.
- Experience with SaaS, hybrid cloud, and AI/ML environments.
- Experience building production-grade agentic workflows, such as multi-agent systems and MCP servers.
- Strong software engineering fundamentals, with deep experience building products and operating large-scale systems.
Compensation and Benefits
The expected base salary range is $168,000–$270,250 USD per year. Compensation is determined based on location, experience, and other objective job-related factors. The position also includes eligibility for equity and benefits.
Applications will be accepted at least until November 6, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is an equal opportunity employer committed to an inclusive work environment.