As a Senior DevOps Engineer, you’ll help turn agentic AI capabilities for diagnosing and troubleshooting network and GPU infrastructure into secure, scalable, production-ready services. This role stands out through its end-to-end ownership across cloud and customer-managed environments, close partnership with software and AI engineers, and direct influence on the reliability of NVIDIA’s AI infrastructure.
What You'll Be Doing:
Own the DevOps, infrastructure, security, release, and reliability lifecycle - from development environments and CI/CD through deployment, production readiness, and sustained operations.
Build and operate Kubernetes environments and Helm-based deployments for a Python, FastAPI, Node.js, and React microservices platform across SaaS and on-premises footprints.
Engineer GitLab CI/CD pipelines with automated testing, container builds, vulnerability scanning, and versioned image and Helm chart publication through JFrog Artifactory.
Automate infrastructure provisioning, configuration, upgrades, and routine operational workflows to accelerate delivery and improve engineering productivity.
Operate PostgreSQL, Temporal workflow services, and S3-compatible object storage with disciplined capacity planning, backups, recovery testing, and safe migrations.
Strengthen release reliability through deployment validation, reduced-downtime strategies, persistent-state protection, and recovery plans for active workflows.
Deliver actionable observability and security using OpenTelemetry, Datadog/Grafana, Langfuse, secrets management, identity integration, TLS, Kubernetes RBAC, network policies, and container hardening.
Partner with software and AI engineers to troubleshoot distributed systems, investigate incidents, define reliability targets, and improve platform performance, resource efficiency, and customer outcomes.
What We Need to See:
Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent experience.
5+ years of experience in DevOps, site reliability engineering, or platform engineering supporting distributed applications and microservices.
Strong Linux administration skills and proficiency in Python and Bash for automation, plus experience with infrastructure as code and configuration tooling such as Terraform and Ansible.
Experience building and maintaining CI/CD pipelines, including runners, container registries, artifact management, automated quality gates, and secure release practices.
Practical experience operating PostgreSQL or comparable relational databases, including SQL, migrations, backup and restore, and performance troubleshooting.
Strong networking and observability fundamentals across TCP/IP, DNS, HTTP, TLS, load balancing, ingress, metrics, logs, traces, dashboards, and actionable alerting.
Sound understanding of secure infrastructure operations and incident response, with demonstrated ownership, cross-functional collaboration, and prioritization in an evolving environment.
Ways To Stand Out From the Crowd:
Experience operating AI applications, agent platforms, or LLM services, including monitoring latency, failures, token usage, and cost.
Familiarity with Temporal, LangGraph, Model Context Protocol (MCP), Langfuse, ClickHouse, Redis/Valkey, or S3-compatible storage.
Deep experience with OpenTelemetry instrumentation and collectors, Datadog APM, or Prometheus/Grafana.
Experience with self-hosted Kubernetes, OpenShift, Kubernetes operators, CloudNativePG, or GPU clusters and AI data centers.
Experience building reproducible AMD64 and ARM64 container images, optimizing BuildKit pipelines, and securing the software supply chain.
With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you are passionate about building mission-critical systems at the frontier of AI infrastructure, we want to hear from you.
