platform engineering, DevOps, SRE, or a closely related infrastructure role.
Production experience with Kubernetes. Not just deploying to it, but operating it: upgrades, RBAC, networking, storage, troubleshooting a cluster that is misbehaving.
Solid CI/CD experience with a modern toolchain (GitHub Actions, GitLab CI, Jenkins, or similar), including building pipelines from scratch.
Experience architecting, deploying, and maintaining a GitOps workflow is an asset.
Hands-on experience with at least one major cloud provider (AWS, GCP, or Azure) and infrastructure as code. Familiarity with on-premise infrastructure is a plus.
Strong Linux fundamentals and comfort with a scripting or programming language such as Python, Go, or Bash.
Working knowledge of containers beyond the basics: image layering, registries, runtime security, minimal base images.
Fluency in written and spoken English, French is a strong asset.
Experience supporting ML or research workloads: GPU scheduling, distributed training, large datasets, high-throughput storage is an asset.
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