Senior DevOps Engineer, AI & Applications

Role Summary

Every AI feature we ship touches thousands of GPUs. The Senior DevOps Engineer will build the release engineering backbone—CI/CD pipelines, automated testing gates, one-click deployments with instant rollback—that lets Firmus scale fast and responsibly. 

You're the bridge between engineering and operations: setting Firmus standards for how code gets to production, mentoring the team on deployment safety, and driving a blameless culture when things go wrong. Ship safely. Ship often. Ship at scale.

Key Responsibilities

• Design and maintain team-wide CI/CD pipelines (Jenkins, GitHub Actions, ArgoCD, or equivalent) with automated testing gates, artifact management, and deployments aligned with GPU cluster standards.
• Implement release engineering best practices: repeatable releases, GitOps workflows, automated rollback, and change management procedure.
• Build and manage test infrastructure: environment provisioning, data seeding, long-running job validation (especially for distributed training templates and multi-node job submissions).
• Establish engineering protocols and standards: repo organization, PR templates, code quality gates, dependency scanning, static analysis.
• Partner with infra teams to ensure AI product features deployment practices meet compliance and security standards for massive GPU clusters.
• Mentor team on testing strategies, deployment safety, and incident response procedures.

Skills & Experience

• 5–7 years of CI/CD engineering, release engineering, or DevOps experience
• Deep expertise in GitHub Actions, GitLab CI, ArgoCD, or Jenkins with multi-stage pipeline design and testing gate implementation.
• Strong automation scripting (Python, Go, or Bash) for build orchestration and environment templating.
• Strong Kubernetes fundamentals (hands-on): deep understanding of Pod lifecycle and failure modes (Pending/Running/CrashLoopBackOff/Evicted), Deployments/ReplicaSets, Jobs/CronJobs, Services/Ingress, and how these primitives behave under load and during rollouts.
• Config & secret management: practical experience designing and operating ConfigMaps and Secrets (including secret rotation patterns), with strong hygiene around least privilege, auditability, and preventing credential leakage into logs/artifacts.
• Safe rollout patterns: proven experience implementing and operating safe rollout strategies (rolling updates, canary, blue/green), readiness/liveness/startup probes, PodDisruptionBudgets, and rollback procedures—ensuring zero/low-downtime deployments for customer-facing services.
• Deployment safety & debugging: ability to debug common Kubernetes rollout issues end-to-end (bad probes, misconfigured resources/limits, image pull failures, secret/config drift, node pressure/evictions) and convert learnings into automated CI/CD gates and runbooks.
• Familiarity with artifact management, versioning strategies, and rollback procedures.
• Experience integrating testing frameworks into CI pipelines (unit, integration, end-to-end).

Key Competencies

• Engineering Velocity & Time-to-Release improves quarter-over-quarter while release standards remain consistent (gates, tests, approvals, auditability).
• Platform Reliability & Customer Trust remains strong: release-related incidents are rare and recovery is fast; reliability targets are met without "surprise outages."
• Developer Productivity & Team Scale improves: engineers spend less time fighting CI/CD and more time shipping as the team grows.
• Cost Efficiency & Resource Optimization improves: CI/CD and test infrastructure costs stay controlled (or decrease per unit of output) as usage scales.
• Knowledge & Culture Multiplier effect is visible: release/reliability practices become the default across the org and repeat incident classes reduce

Success Metrics

• Engineering Velocity & Time-to-Release improves quarter-over-quarter while release standards remain consistent (gates, tests, approvals, auditability).
• Platform Reliability & Customer Trust remains strong: release-related incidents are rare and recovery is fast; reliability targets are met without “surprise outages.”
• Developer Productivity & Team Scale improves: engineers spend less time fighting CI/CD and more time shipping as the team grows.
• Cost Efficiency & Resource Optimization improves: CI/CD and test infrastructure costs stay controlled (or decrease per unit of output) as usage scales.
• Knowledge & Culture Multiplier effect is visible: release/reliability practices become the default across the org and repeat incident classes reduce

Location & Reporting

• Singapore or Australia (Launceston, TAS or Sydney, NSW)
• Reporting to Head of AI & Applications

 
Employment Basis

Full-time

Diversity

At Firmus, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions. 

Join us in our mission to revolutionize the AI industry through sustainable practices and cutting-edge engineering. Apply now to be part of shaping the future of sustainable AI infrastructure.