Senior MLOps Engineer, Dailymotion

• 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
• Don't check every box? Apply anyway

Desirable

• Our ML team runs +100 active AI projects on GCP and needs the industrialized infrastructure to match
• ML team is moving fast and they need a dedicated expert to connect the dots, align on the right practices, and help both sides deliver at their best. You'll be that person
• You'll work at the heart of an AI-first environment, where shipping models fast and reliably is the core business, not a side project
• 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, not just consuming them
• Keep ML Engineers in control of their models in production: monitor, troubleshoot, iterate, refine directly in prod, no staging/prod mirror
• 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, you've seen L40 shortages mid-training, you know how to handle them
• 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

• HR Interview with Marvin (30mn)
• Manager interview with Cédric & Thierry (1h)
• Technical Interview with 2 Architects (1h) : no coding test, just a real technical conversation
• ML Interview with Brice & Samuel (1h)
• Final Interview with Alan, VP Platform (1h)