15 days ago
Ra‘ananna, IsraelSenior
Responsibilities
- Build packaging, configuration, environment validation, upgrade, rollback, and runtime-safeguard capabilities for the deployment platform.
- Own deployability across Kubernetes, cloud, on-premises, restricted, and air-gapped environments.
- Develop 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 and remain correct during long-term operation.
- Improve platform resilience for partial failures, misconfiguration, restarts, state loss, and degraded environments.
- Translate scale and benchmark findings into deployment guidance, sizing recommendations, and supported topologies.
- Help migrate the platform from legacy deployment models to a modern Kubernetes-based operational model.
Requirements
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- At least 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 languages.
- 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.
- Experience with on-premises, customer-controlled, or air-gapped environments is preferred.
- Experience with Kubernetes deployability at scale, including upgrades, rollback, configuration management, and day-two operations is preferred.
- Familiarity with telemetry, observability, monitoring, or AI/HPC infrastructure is preferred.
- Experience productizing operational solutions such as pre-install validation, deployment standards, support tooling, or automated recovery paths is preferred.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.