Senior DevOps Engineer, AIOPs

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is seeking a Senior DevOps Engineer to operate its AI Data Center AIOps platform. This role focuses on maintaining the platform's uptime, performance, data integrity, and safe change management. The engineer will be responsible for SLOs/SLIs, incident response, and postmortems related to telemetry ingestion, processing, storage, and APIs/dashboards. This position involves collaboration with Software Engineering and Systems Engineering teams to translate platform signals into actionable alerts and automation. The goal is to ensure the reliability and efficiency of the platform that operators depend on.

Requirements

BS/MS in CS/CE (or equivalent experience) and 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 (Python/Bash), 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 Haves

Strong Linux + networking fundamentals, distributed systems instincts, and hands-on ops for Kubernetes/services/streaming stacks are ideal.
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 replay/backfill pipelines with correctness checks.
Strong 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.

Responsibilities

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, consistent, and reproducible.
Contribute in adjacent functional areas to grow and help your team members.