NVIDIA is powering the world’s most advanced AI Factories. Keeping them running depends on NVIDIA’s AI Ops platform for large-scale AI infrastructure — continuously ingesting telemetry from GPUs, NICs, switches, and links across the fleet, and delivered both as a high-scale SaaS service and as self-contained on-premises software for enterprise customers.

We are looking for a Senior Software Engineer to help make this platform deployable, operable, and scalable across customer-controlled environments ranging from a single node to tens of thousands of GPUs. This role sits at the intersection of platform software and runtime reality: packaging, configuration, deployment automation, upgrades, survivability, and large-scale operational correctness.

What you’ll be doing:

• Build the deployment platform underneath the product: packaging, configuration, environment validation, upgrade paths, rollback flows, and runtime safeguards.
• Own deployability across Kubernetes, cloud, on-prem, and restricted or air-gapped environments.
• Design and improve the mechanisms that make the system installable and supportable at scale: Helm charts, deployment manifests, configuration schemas, dependency validation, readiness checks, and operational tooling.
• Partner with backend and feature teams to ensure services are production-ready on day 1 and still correct on day 400.
• Improve platform resilience: design for partial failure, misconfiguration, restarts, state loss, and degraded environments.
• Turn scale and benchmark findings into concrete deployment guidance, sizing recommendations, and supported topologies.
• Help evolve the platform from legacy deployment models toward a modern Kubernetes-based operational model.

What we need to see:

• B.Sc. or M.Sc. in n Computer Science, Computer Engineering, or a related technical field (Courses, Eligibility, Fees & Verified Programs), or equivalent practical experience.
• 5+ years of software engineering experience in platform, infrastructure, runtime, or deployability-focused roles.
• Hands-on experience with Kubernetes and production deployment tooling such as Helm, operators, or GitOps workflows.
• Strong programming and automation skills in one or more of Go, Python, Bash, or similar.
• Experience shipping software that other organizations install and operate themselves.
• Practical experience with distributed systems and production services that fail independently and must remain correct under partial failure.
• Strong debugging skills across Linux, networking, containers, and service runtime behavior.
• Experience with CI/CD, release engineering, environment validation, or infrastructure-as-code.

Ways to stand out from the crowd:

• Experience delivering software into on-prem, customer-controlled, or air-gapped environments.
• Experience with Kubernetes deployability at scale, including upgrades, rollback, configuration management, and day-2 operations.
• Familiarity with telemetry, observability, monitoring, or AI/HPC infrastructure.
• Experience turning operational pain points into productized solutions: pre-install validation, deployment standards, support tooling, or automated recovery paths.
• A systems mindset: you can move comfortably between code, runtime, infrastructure, and customer reality.