NVIDIA
AI
SeniorSiteReliabilityEngineer,AIOPs
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Site Reliability Engineer, AIOPs at NVIDIA. Skills: Site Reliability Engineering, AIOPs platform operation, Kubernetes, Automation, Infrastructure-as-code, Observability. Continuously monitor platform health via dashboards/logs/metrics. Automate recurring checks”
What You'll Achieve.
Reliable, job-centric insights and automation for GPU fleets; Uptime, performance, data integrity, and safe change management for the AIOPs platform; Dependable customer-facing reliability
Industry & Context.
Identify likely root causes; Reason about backpressure, hotspots, and failure domains end-to-end
On-call responsibilities implied by SRE role, NVIDIA uses AI tools in its recruiting processes
What They're Looking For.
Must Have
5+ years operating production distributed systems as SRE/DevOps/Platform Ops, Proven ownership of reliability for an observability/AIOps platform: SLOs/SLIs, on-call, addressing incidents, and follow-up evaluations that drive measurable improvements, Deep Kubernetes + containers experience (deploying, debugging, scaling) for telemetry-heavy microservices—ingestion, processing, storage, APIs, and UI, Automation-first approach: solid scripting (Pythonash), CI/CD, and infrastructure-as-code (Terraform + Helm) to deliver safe rollouts (canaries/rollbacks), reproducible environments, and minimal toil, Clear communicator who writes excellent runbooks/docs and can translate ambiguous requirements into concrete operational practices and dependable customer-facing reliability
Nice to Have
Linux + networking fundamentals, distributed systems instincts, and hands-on ops for Kubernetes/services/streaming stacks are bonus for experience with observability platforms at scale, Experience building safe automation that operators trust: canary releases, automated rollback criteria, “monitoring for the monitoring” (lag/drop/error budgets), and replayackfill pipelines with correctness checks, in distributed/streaming systems operations (Kafka/Pulsar, Flink/Spark, ClickHouse/Elastic/TSDBs, object storage)—and can reason about backpressure, hotspots, and failure domains end-to-end, Proven programming experience building automation tools or services — ideally in Python, or similar languages — to simplify operations and scale recurring processes, Proven experience running large‑scale production deployments and multiple Kubernetes environments or clusters across teams or customers, coordinating changes and rollouts with minimal disruption with hands‑on experience with observability tools — you know your way around dashboards, metrics, logs, and traces using platforms like Prometheus, Grafana, or similar
What You'll Do.
Continuously monitor platform health via dashboards/logs/metrics
Automate recurring checks
Keep reliability + resource efficiency on track
Own Kubernetes deployments end-to-end (runbooks
post-deploy validation)
Lead rollbacks/remediations when needed
Lead first-level incident triage
Build and maintain runbooks/SOPs/checklists
Manage deployment infrastructure and packaging
Contribute in adjacent functional areas
How You'll Work.
Team & Collaboration
Partner with Software Engineering and Systems Engineering teams; Translate platform signals into actionable, trustworthy alerts and automation; Grow and help team members
Communication Scope
Clear communicator; Writes excellent runbooks/docs; Translate ambiguous requirements into concrete operational practices
Full Job Description
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Join our team of innovative engineers who are building an AI Data Center AIOps platform that turns raw, high-volume telemetry into reliable, job-centric insights and automation for GPU fleets. We’re hiring a DevOps Engineer to operate the platform itself (not the compute cluster): uptime, performance, data integrity, and safe change management. You’ll own SLOs/SLIs, incident response, and postmortems for the telemetry ingestion, processing, storage, and APIs/dashboards that operators depend on. You’ll partner Software Engineering and Systems Engineering team to translate platform signals into actionable, trustworthy alerts and automation. **What you 'll be doing:** * Continuously monitor platform health via dashboards/logs/metrics, automate recurring checks, and keep reliability + resource efficiency on track. * Own Kubernetes deployments end-to-end (runbooks, canary checks, post-deploy validation), and lead rollbacks/remediations when needed. * Lead first-level incident triage: collect diagnostics, identify likely root causes, and hand off clear, actionable findings to engineering. * Build and maintain runbooks/SOPs/checklists, pushing continuous improvement through automation. * Manage deployment infrastructure and packaging (Helm + Terraform/IaC) to keep environments scalable, con
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