Join NVIDIA AI Enterprise as a Senior Software Solutions Engineer, a dynamic role focused on supporting customer deployments in cloud and datacenter environments. You will be instrumental in resolving complex software issues end-to-end while also developing new software features, automation, diagnostics, and deployment tooling. This position is crucial for enhancing product readiness and scaling support across enterprise ecosystems.

This role involves working with cutting-edge compute and cloud-native technologies. You'll engage with container platforms, enterprise system software, and GPU-accelerated AI frameworks that power large-scale production AI workloads. As a customer-facing engineer, you'll collaborate closely with clients and internal teams to diagnose issues, communicate root causes, drive resolutions, and contribute to product improvements. Exceptional debugging abilities, clear communication, and a proactive approach to resolving technical escalations are key to success.

Develop and maintain essential features and deployment assets for AI Enterprise supportability, including scripts, configuration guides, Kubernetes manifests, Helm charts, and reproducible test cases.

Create and sustain Python-based automation tools, such as validators and log collectors, to bolster NVIDIA AI Enterprise deployment reliability across NGC and container orchestrators like Kubernetes.

Contribute to code-level fixes, patches, and pull requests in partnership with engineering teams to address customer-impacting issues and enhance product stability.

Provide robust support to enterprise clients deploying NVIDIA AI Enterprise in datacenter and cloud service provider environments, focusing on Kubernetes-based and containerized AI platforms.

Take full ownership of customer issues from their initial report to final resolution: reproduce challenges in a lab or cloud setting, gather critical diagnostics, propose mitigations, and collaborate with engineering on effective fixes.

Generate high-quality bug reports and feature requests (RFEs) complete with clear reproduction steps, detailed environment specifics (CSP, Kubernetes, GPU), impact analysis, and supporting documentation.

Author customer-facing and internal documentation, such as knowledge base articles, runbooks, and deployment guides, to expedite time-to-value and minimize recurring problems.

Participate in an on-call rotation, approximately one weekend per month, to provide engineering assistance during critical Sev1 customer outages.

A Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, or a closely related field is required, or equivalent practical experience.

Possess at least five years of experience in system software development and troubleshooting, with a preference for prior customer-facing roles.

Demonstrate strong computer science fundamentals and proficiency in programming and scripting, with a mandatory requirement for Python skills; Bash, Go, or C++ experience is a valuable asset for automating investigations and developing diagnostics.

Exhibit solid troubleshooting acumen in networking, concurrency, and operating system concepts, coupled with a structured methodology for isolating issues across application, platform, and infrastructure layers.

Possess a deep understanding of at least two of the following areas: datacenters/servers, distributed systems, virtualization, deep learning frameworks, containers (Docker/Kubernetes), hybrid cloud environments (AWS/Azure/GCP), and CI/CD practices for dependable deployments.

Familiarity with GPU-accelerated AI/ML stacks and the deployment/serving of production models, including experience with NGC containers, CUDA tooling, and inference serving technologies like Triton.

Exhibit extensive knowledge of Linux environments and comfort in troubleshooting production Linux systems; a working knowledge of Windows is considered a plus.

Showcase professional-level communication and interpersonal skills, driven by a genuine passion for problem-solving.