Description
We are seeking a Senior Software Solutions Engineer to support NVIDIA AI Enterprise customers and deployments across cloud and datacenter environments. This role involves two main responsibilities: (1) providing end-to-end support, triage, and resolution of complex customer software issues, and (2) building software features, automation, diagnostics, and deployment tooling to enhance product readiness and scalability across enterprise environments.
In this customer-facing role, you will collaborate with customers and internal engineering teams to identify issues, determine root causes, drive resolutions, and develop fixes and improvements. Strong debugging skills, effective communication, and technical expertise are essential for success in this position.
Key Responsibilities:
- Develop and maintain product-facing features and deployment assets for AI Enterprise supportability, including scripts, configuration guidance, Kubernetes manifests, and reproducible test cases.
- Create Python-based tooling and automation to improve NVIDIA AI Enterprise deployment reliability across NGC and container orchestrators like Kubernetes.
- Contribute code-level fixes, patches, or pull requests in collaboration with engineering teams to address customer-impacting issues and enhance product readiness.
- Support enterprise customers deploying NVIDIA AI Enterprise in datacenter and cloud environments, including Kubernetes-based and containerized production AI platforms.
- Take ownership of customer issues from inception to resolution, reproducing issues in lab/cloud environments, collecting diagnostics, providing mitigations, and partnering with engineering teams on fixes.
- Develop high-quality bug reports and RFEs with clear repro steps, environment details, impact analysis, and supporting artifacts.
- Create customer-facing and internal documentation to improve time-to-value and reduce recurring issues.
- Participate in on-call rotations, including one weekend per month, to provide engineering assistance for critical customer outages.
Requirements:
- BS in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).
- 5+ years of system software development and troubleshooting experience, preferably with some customer-facing experience.
- Strong computer science fundamentals and programming/scripting skills, particularly in Python, with Bash and Go/C++ being a plus.
- Strong troubleshooting fundamentals, including networking, concurrency, and OS concepts, with a structured approach to isolating issues across application, platform, and infrastructure layers.
- Deep understanding of at least two of the following areas: 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, such as NGC containers, CUDA/tooling concepts, and inference serving like Triton.
- Deep Linux knowledge and comfort troubleshooting in production Linux environments; working knowledge of Windows is a plus.
- Professional-level communication and interpersonal skills, with a passion for problem-solving.
Preferred Qualifications:
- Hands-on experience deploying and operating NVIDIA AI Enterprise components in production across on-prem or cloud environments.
- Experience using AI coding assistants/tools to accelerate debugging, automation, and test creation.
- Experience operating Kubernetes-based platforms in production, including cluster operations, upgrades, and control-plane/data-plane failure modes.
- Strong performance debugging skills for GPU and cloud workloads, including profiling, latency/throughput tuning, and familiarity with observability/tracing tools.