Senior DevOps Engineer (Full Remote from France)

Dailymotion

9d•Remote

About The Position

Dailymotion is seeking its first dedicated DevOps/MLOps Engineer to support its ML team, which runs over 100 active AI projects on GCP. The role involves empowering ML Engineers with the necessary tools and infrastructure for rapid iteration, accelerating the time-to-market for ML products, and owning ML CI/CD. The engineer will ensure ML Engineers maintain control over their models in production, enable large-scale ML experimentation, and deliver MLOps building blocks while managing GPU infrastructure. This position also includes tackling technical debt, acting as a technical mediator between ML and Backbone teams, and handling run responsibilities such as on-call and post-mortems.

Requirements

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.
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.

Nice To Haves

Go or Rust a plus.
Jupyter Notebooks, broader ML/AI ecosystem
Data pipelines (Airflow, Dataflow, Kestra)
Redis clusters and infrastructure performance optimization

Responsibilities

Empower ML Engineers with the tools, infrastructure, and frameworks they need to iterate fast autonomously.
Accelerate time-to-market for production-ready ML products: seamless integration, proper service connections, access to data and resources.
Own ML CI/CD in close collaboration with the ML team, adapting existing frameworks to ML-specific needs.
Keep ML Engineers in control of their models in production: monitor, troubleshoot, iterate, refine directly in prod.
Enable large-scale ML experimentation: robust, reproducible, scalable environments for both internal tests and A/B testing in production.
Deliver concrete MLOps building blocks (MLflow, Kubeflow, KubeRay...) and manage GPU infrastructure dynamically.
Tackle technical debt on existing projects while laying the right foundations for what's next.
Be the technical mediator between ML and Backbone teams understand both sides, propose solutions that stick.
Handle run responsibilities: on-call, post-mortems, level-1 failure analysis.