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.