Solid MLOps or DevOps, background projects shipped in prod matter more than years on a resume
GCP expert: Vertex AI, GKE, GCS, BigQuery
Full GitOps: FluxCD first, ArgoCD accepted
Kubernetes in prod, not just in a lab
Hands-on with real MLOps tools: MLflow, Kubeflow, KubeRay
GPU-aware: you've managed GPU scarcity at scale during mass training runs
Python is a must, Bash expected, Go or Rust a plus
IaC (Terraform), containerization (Docker, Helm), observability (Prometheus, Datadog, Looker)
AI Coding Assistants (Claude, Cursor, Dust)
Data lifecycle management (cost, security, encryption)
Fluent in French and English
Jupyter Notebooks, broader ML/AI ecosystem
Data pipelines (Airflow, Dataflow, Kestra)
Redis clusters and infrastructure performance optimization
What Will Make the Difference
Beyond the tech stack, what will set you apart is your ability to act as a bridge rather than a silo. You thrive in ambiguity, stay calm under pressure, and turn complexity into clear solutions. You have a run culture, post-mortems and on-call sharpen you rather than drain you. And you lead by example: you advocate for best practices by winning people over, not by imposing them
Additional Information
Don't check every box? Apply anyway
We're looking for the right person, not the perfect resume. If this role excites you, let's talk