3 days ago
Pune, IndiaSenior
Responsibilities
- Develop and maintain product-facing supportability features, scripts, configuration guidance, Kubernetes manifests, Helm charts, and reproducible test cases.
- Build Python-based validators, log collectors, repro harnesses, and other automation for NVIDIA AI Enterprise deployments.
- Contribute code fixes, patches, and pull requests with engineering to resolve customer-impacting issues and improve product readiness.
- Support customers deploying NVIDIA AI Enterprise across datacenter and cloud service provider environments, including Kubernetes-based production platforms.
- Own customer issues end-to-end by reproducing problems, collecting diagnostics, providing mitigations, and partnering with engineering on fixes.
- Create detailed bug reports and requests for enhancement with reproduction steps, environment details, impact analysis, and supporting artifacts.
- Develop customer-facing and internal knowledge-base articles, runbooks, and deployment guidance.
- Provide engineering assistance during Sev1 customer outages, including one weekend of on-call coverage per month.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent experience.
- At least 5 years of system software development and troubleshooting experience, ideally including customer-facing work.
- Strong programming and scripting skills with Python required; Bash required or expected, with Go and C++ as advantages.
- Strong troubleshooting fundamentals in networking, concurrency, and operating-system concepts, with the ability to isolate issues across application, platform, and infrastructure layers.
- Deep understanding of at least two areas including datacenters and servers, distributed systems, virtualization, deep learning frameworks, Docker/Kubernetes containers, hybrid cloud, or reliable deployment practices.
- Familiarity with GPU-accelerated AI/ML stacks and production model deployment and serving, including NGC containers and CUDA concepts.
- Deep Linux knowledge and comfort troubleshooting production Linux environments; Windows knowledge is a plus.
- Professional-level communication and interpersonal skills with a strong problem-solving orientation.
- Preferred experience deploying and operating NVIDIA AI Enterprise in production across on-premises or cloud environments.
- Preferred experience operating Kubernetes platforms in production, including cluster operations, upgrades, and control-plane or data-plane failure modes.
- Preferred GPU and cloud performance-debugging experience, including profiling and latency or throughput tuning, plus familiarity with observability and tracing tools.
- Preferred experience using Cursor, Claude Code, Codex, or similar AI coding assistants for debugging, automation, and test creation.
Benefits
- One weekend of on-call support per month is required for Sev1 customer outages and engineering assistance.
Tech Stack
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.