Staff DevOps Engineer: Industrial AI Infra at Scale

Nexxa.ai

Sunnyvale (CA)

On-site

USD 200,000 - 260,000

Full time

7 days ago

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Benefits offered by this job

Equity

Competitive compensation

Job summary

Nexxa.ai in Sunnyvale, CA is hiring a Senior/Staff DevOps Engineer to own and scale the infrastructure powering AI and industrial systems. You will run GPU-backed training and inference clusters, design reproducible environments, and partner with AI/data teams to keep production workloads fast and reliable.

You will build pipelines, manage Kubernetes platforms, and drive observability, security, and reliability across cloud and on-prem deployments.

Qualifications

6+ years of experience in DevOps, Site Reliability Engineering, Platform Engineering, or infrastructure-focused software engineering roles.
Deep hands-on experience with Cloud platforms (AWS, GCP, or Azure) at production scale.
Kubernetes in production, including GPU workload scheduling.
Infrastructure-as-code tooling (Terraform, Pulumi, or equivalent).
CI/CD systems (e.g., GitHub Actions, GitLab CI, CircleCI, Jenkins, ArgoCD).
Strong track record designing and operating observability stacks (Prometheus, Grafana, Datadog, OpenTelemetry).
Experience supporting ML/AI infrastructure — training clusters, model serving, data pipelines — a strong plus.
Excellent scripting/programming skills (Python, Go, or Bash).
Proven ability to independently scope and lead infrastructure projects from design through production rollout.
Strong incident management instincts — you can lead through an outage calmly and drive toward root cause.

Responsibilities

Own and evolve Nexxa's core infrastructure — compute, networking, storage, and deployment systems — end-to-end.
Design and operate CI/CD pipelines that support fast, safe iteration across AI, data, and product engineering teams.
Build and maintain infrastructure-as-code (e.g., Terraform, Pulumi) for reproducible, auditable environments across cloud and on-prem/edge deployments.
Architect and manage Kubernetes-based platforms for training, inference, and application workloads, including GPU scheduling and autoscaling.
Partner with data and AI teams to support the infrastructure behind data warehouses and lakehouse architectures, feature stores, embedding indices, and retrieval pipelines.
Define and drive observability practices — metrics, logging, tracing, and alerting — across distributed systems.
Establish and enforce reliability practices: SLOs/SLIs, incident response, postmortems, and on-call rotations.
Design for security and compliance across cloud infrastructure, secrets management, and access control.
Make pragmatic tradeoffs across cost, latency, reliability, and developer velocity.
Collaborate with engineering leadership to define infrastructure roadmap and platform strategy.
Mentor engineers on infrastructure best practices and raise the bar for operational excellence across the org.