Senior Software Engineer – Platform and Deployability

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.