We are looking for a Software Solutions Engineer to support NVIDIA AI Enterprise customers and deployments across cloud and datacenter environments. This is a dual role: (1) Support, triage and resolve complex customer software issues end-to-end, and (2) build software features, automation, diagnostics, reproducible test cases, and deployment tooling—to improve product readiness and scale support across enterprise environments.

You will work across compute and cloud-native technologies in CSP environments, including container platforms/orchestrators, enterprise system software, and GPU-accelerated AI frameworks and inference services used to run production AI workloads at scale. In this customer-facing role, you will work closely with customers and internal engineering teams to understand issues, explain root causes, drive resolution, and collaborate on fixes and improvements. Success in this role requires strong debugging skills, crisp communication, and ownership of technically deep escalations from inception to closure.

What you'll be doing:

• Develop and maintain product-facing features and deployment assets for AI Enterprise supportability (e.g., scripts, configuration guidance, Kubernetes manifests/Helm charts, and reproducible test cases)

• Develop and maintain Python-based tooling/automation (validators, log collectors, repro harnesses) to improve NVIDIA AI Enterprise deployment reliability across NGC and container orchestrators (e.g., Kubernetes)

• Contribute code-level fixes, patches, or pull requests (as appropriate) in collaboration with engineering to address customer-impacting issues and improve product readiness

• Support enterprise customers deploying NVIDIA AI Enterprise in datacenter and CSP environments, including Kubernetes-based and containerized production AI platforms

• Take ownership of customer issues from inception to resolution: reproduce in lab/cloud, collect diagnostics, provide mitigations, and partner with engineering on fixes

• Create high-quality bug reports and RFEs with clear repro steps, environment details (CSP/Kubernetes/GPU), impact analysis, and supporting artifacts

• Develop customer-facing and internal documentation (KBs, runbooks, deployment guidance) to improve time-to-value and reduce recurring issues

• Be on call one weekend per month in the event a customer has a Sev1 outage and requires engineering assistance

What we need to see:

• BS in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience)

• At least 5+ years system software development and troubleshooting experience, ideally with some customer facing

• Strong computer science fundamentals and programming/scripting skills (Python required; Bash; Go/C++ a plus) to automate investigations and build diagnostics/repro tools

• Strong troubleshooting fundamentals (networking, concurrency, OS concepts) and a structured approach to isolating issues across application, platform, and infrastructure layers

• Deep understanding of at least two of the following: data centers/servers, distributed systems, virtualization, deep learning frameworks, containers (Docker/Kubernetes), hybrid cloud (AWS/Azure/GCP), and CI/CD for reliable deployments

• Familiarity with GPU-accelerated AI/ML stacks and production model deployment/serving (e.g., NGC containers, CUDA/tooling concepts, inference serving such as Triton or similar)

• Deep Linux knowledge and comfort troubleshooting in production Linux environments; working knowledge of Windows is a plus

• Professional-level communication skills, interpersonal skills with a passion to solve problems

Ways to stand out from the crowd:

• Hands-on experience deploying and operating NVIDIA AI Enterprise components in production across on-prem or CSP environments

• Hands-on experience using AI coding assistants/tools (e.g., Cursor, Claude Code, Codex, or similar) to accelerate debugging, automation, and test creation

• Experience operating Kubernetes-based platforms in production (cluster operations, upgrades, control-plane/data-plane failure modes)

• Strong performance debugging skills for GPU and cloud workloads (profiling, latency/throughput tuning) and familiarity with observability/tracing tools