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Strong Tier-1 brand, metro location, and mid-level generalist role increase candidate competition.
Core devops skills transfer across industries, but GPU/AI specialization increases domain specificity.
Explicit 5+ years plus mandatory Kubernetes, Linux, Python, and GPU/AI experience increases strictness.
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Support and resolve complex software issues for NVIDIA AI Enterprise customers across cloud and datacenter environments, owning problems end-to-end.
Develop and maintain automation, diagnostics, reproducible test cases, and deployment tooling to improve product readiness and scale enterprise support.
Collaborate closely with customers and internal engineering teams to troubleshoot, communicate root causes, and contribute code fixes or enhancements related to AI Enterprise deployments.
Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience).
Minimum 5+ years of system software development and troubleshooting experience with some customer-facing exposure.
Proficient programming/scripting skills in Python; familiarity with Bash, Go, or C++ is a plus.
Deep understanding of at least two areas: data centers/servers, distributed systems, virtualization, deep learning frameworks, containers (Docker/Kubernetes), hybrid cloud (AWS/Azure/GCP), and CI/CD for reliable deployments.
Experienced in deploying and operating NVIDIA AI Enterprise or similar GPU-accelerated AI platforms in production datacenter or cloud service provider environments.
Strong troubleshooting skills across application, platform, and infrastructure layers, with ability to reproduce, diagnose, and mitigate complex issues including in Linux production environments.
Proficient in building automation and diagnostic tooling (Python-based), with demonstrated ability to collaborate with engineering on code fixes and handle high-severity incidents (on-call participation).