About the RoleWe'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
