Senior DevOps Engineer — full remote from France

Dailymotion is hiring its first dedicated DevOps / MLOps Engineer to join an AI-first environment. The ML team runs more than 100 active AI projects on GCP and needs robust, scalable infrastructure to support fast iteration. You will be at the center of connecting tools, infrastructure, and frameworks so ML engineers can ship models quickly and reliably.

Your mission: - 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, access to data, and resource provisioning. - Own ML CI/CD in close collaboration with the ML team, adapting existing frameworks to ML-specific needs. - Keep ML models in production observable and controllable: monitor, troubleshoot, iterate directly in production. - Enable large-scale ML experimentation with robust, reproducible, scalable environments for internal tests and A/B testing in production. - Deliver concrete ML building blocks (MLflow, Kubeflow, KubeRay) and manage GPU infrastructure dynamically, addressing GPU shortages during training. - Tackle technical debt on existing projects while laying the foundations for what's next. - Act as the technical mediator between ML and Backbone teams, proposing solutions that stick. - Handle run responsibilities: on-call, post-mortems, Level-1 failure analysis.

Senior DevOps Engineer — full remote from France

Dailymotion is hiring its first dedicated DevOps / MLOps Engineer to join an AI-first environment. The ML team runs more than 100 active AI projects on GCP and needs robust, scalable infrastructure to support fast iteration. You will be at the center of connecting tools, infrastructure, and frameworks so ML engineers can ship models quickly and reliably.

Your mission: - 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, access to data, and resource provisioning. - Own ML CI/CD in close collaboration with the ML team, adapting existing frameworks to ML-specific needs. - Keep ML models in production observable and controllable: monitor, troubleshoot, iterate directly in production. - Enable large-scale ML experimentation with robust, reproducible, scalable environments for internal tests and A/B testing in production. - Deliver concrete ML building blocks (MLflow, Kubeflow, KubeRay) and manage GPU infrastructure dynamically, addressing GPU shortages during training. - Tackle technical debt on existing projects while laying the foundations for what's next. - Act as the technical mediator between ML and Backbone teams, proposing solutions that stick. - Handle run responsibilities: on-call, post-mortems, Level-1 failure analysis.