Staff DevOps Engineer

About the Role
We're looking for a Senior/Staff DevOps Engineer who has spent the last several years building and operating the infrastructure that lets AI and industrial systems run reliably at scale. You understand what it takes to keep production ML and data workloads fast, observable, and resilient — from GPU-backed training and inference clusters to the pipelines that connect them to real-world industrial environments.

This role is ideal for candidates who want deep infrastructure ownership at a company where uptime, latency, and reliability directly affect physical operations — not just software. You'll partner closely with AI, data, and product engineering teams to make sure the systems they build can actually run in production, safely and at scale.

What You'll Do
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 (e.g., Snowflake, BigQuery, Redshift, Databricks)

Feature stores, embedding indices, and retrieval pipelines

Model training, evaluation, and serving infrastructure

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, particularly relevant to industrial and legacy-environment integrations

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

Required 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 (e.g., 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) for automation and tooling

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

Preferred Qualifications
Experience operating infrastructure that bridges cloud and edge/on-prem environments, especially in industrial or manufacturing contexts

Familiarity with data warehouse/lakehouse platforms (Snowflake, BigQuery, Redshift, Databricks)

Experience with service mesh, zero-trust networking, or compliance frameworks relevant to industrial/critical infrastructure (e.g., SOC 2, IEC 62443)

History of building internal developer platforms or self-service infrastructure tooling

Experience scaling infrastructure teams or setting technical direction at a Staff level

What Success Looks Like
You can own ambiguous, high-stakes infrastructure problems end-to-end

Systems you build stay reliable as usage and scale grow — you design for the next order of magnitude, not just today

You bring strong technical judgment on tradeoffs between reliability, cost, and speed

You raise the bar for operational rigor and engineering discipline across the team

You help define what's next for the platform, not just execute what's known