Senior Production Engineer - DGX Cloud
At a glance
Senior Site Reliability role at NVIDIA. Remote, Switzerland · full-time.
Pay not stated Site Reliability salary
remoteroles summary, based on the employer's posting.
What you'll do
- Build and operate software and automation for control planes, model launches, and inference workloads across DGX Cloud environments
- Keep inference platforms dependable through health checks, guarded releases, observability, and recovery
- Maintain endpoint availability and predictable performance by improving request routing, capacity handling, and service health
- Apply infrastructure-as-code and GitOps practices to configure, test, update, and restore services consistently
- Create workflows for releases and service handoffs, define reliability targets, and turn recurring incidents into lasting fixes
What you bring
- At least eight years building or running production services and large distributed systems, including hands-on automation
- Strong Python, Go, or comparable programming skills, with experience creating tools for production operations
- Experience with infrastructure as code, configuration management, or GitOps, and automating repeatable service changes
- Strong grounding in Linux, Kubernetes, containers, cloud systems, distributed computing, and networking, plus production troubleshooting
- A computer science degree or equivalent practical experience
Who this fits
This senior role suits an engineer focused on operating reliable inference services and distributed systems. You’ll join on-call and incident response, and collaborate with teams across engineering. The position is remote in Switzerland.
From the employer
NVIDIA DGX Cloud delivers AI services and endpoints for research and production workloads. We are looking for a Senior Production Engineer to build software and automation that make those services reliable, scalable, and safe to operate. The Production Engineering team works on large-scale distributed systems spanning internal and external model endpoints; regional control plane services that orchestrate workloads and route requests; and the GPU/CPU compute infrastructure where inference and agentic workloads run. Our work spans Kubernetes clusters across AWS, Azure, Google Cloud, other partner cloud environments, and on-premises deployments.
What you’ll be doing:
- Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments.
- Improve the reliability of inference and agentic platforms and services, including NVIDIA Cloud Functions, SGLang- and vLLM-based endpoints, and inference services built with NVIDIA Dynamo, through health validation, safer rollouts, observability, and recovery.
- Improve endpoint availability, inference routing, capacity management, and service health to maintain predictable performance as workloads and demand change.
- Use infrastructure as code and GitOps to deploy, configure, validate, upgrade, and recover services consistently across environments.
- Build workflows for service enablement, model releases, handoff, deprecation, and ongoing operations; replace repeatable manual work with reliable automation.
- Define and instrument SLIs and SLOs for inference and control plane services, including availability and latency, use error budgets to guide reliability improvements, and make production health visible to partner teams.
- Participate in on-call and incident response, troubleshoot failures across routing, model runtimes, software, and infrastructure, and turn recurring issues into automation and durable fixes.
- Collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to design and operate services safely at scale.
What we need to see:
- 8+ years of experience building or operating production services and large-scale distributed systems, including hands-on automation.
- Strong programming skills in Python, Go, or a comparable language, with experience developing tools for production operations.
- Experience with infrastructure as code, configuration management, or GitOps, and with building automation for repeatable service deployments and changes.
- Strong knowledge of Linux, Kubernetes, containers, cloud infrastructure, distributed systems, and networking fundamentals; ability to diagnose failures in production.
- Understanding of Production Engineering principles, including SLIs, SLOs, error budgets, incident response, and reducing operational toil.
- Experience instrumenting services and using metrics, logs, and traces to understand system behavior and improve reliability.
- Clear technical communication and ability to work across engineering teams.
- BS/MS in Computer Science or equivalent practical experience.
Ways to stand out from the crowd:
- Familiarity with technologies such as vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, or NCCL, and with GPU performance analysis.
- Experience building Kubernetes operators, controllers, workload orchestration services, fleet management systems, or self-healing automation.
- Experience with Terraform, Argo CD, CI/CD, policy validation, or safe deployment and rollback systems.
- Background with developing with AI tools and agents.
- Experience with production AI inference or agentic workloads, including debugging issues across models, runtimes, Kubernetes, and hardware.
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hard-working people on the planet working for us. If you're creative, hard-working and self-motivated, we want to hear from you!
Site Reliability work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Site ReliabilityRemote
Find me workBy the team behind remoteroles.