Role Description We're hiring our first dedicated Devops / MLOps Engineer. 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. Qualifications - 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. Requirements - Jupyter Notebooks, broader ML/AI ecosystem. - Data pipelines (Airflow, Dataflow, Kestra). - Redis clusters and infrastructure performance optimization. Benefits - 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. - Dailymotion is an equal opportunity employer. All positions are open to people with disabilities. Need accommodations? Just let us know. Interview Process - 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). Additional Information - Type of Contract: Permanent. - Department: Operations. - Compensation: EUR 75000 - EUR 85000 yearly.

• Owning and evolving CI/CD pipelines using GitHub Actions and/or Azure DevOps to support seamless, automated deployments • Automating build, test, packaging, and deployment processes for .NET applications, database projects, and front-end components • Managing release processes, including branching, versioning, and deployments across development, UAT, and production environments • Maintaining Infrastructure as Code for cloud and on-prem environments to ensure scalability and consistency • Building and maintaining monitoring, alerting, and logging solutions to ensure platform reliability and performance • Supporting incident response, root cause analysis, and continuous improvement of operational processes and runbooks

Role Description Join our engineering team and take ownership of building and scaling reliable, high-performing platforms that power critical financial data solutions used across global markets. This is an opportunity to work on complex, large-scale platforms where your work directly impacts the reliability and performance of systems used by asset managers and advisory firms worldwide. You will gain hands-on experience with modern DevOps practices, cloud technologies, infrastructure automation, and high-availability systems, while collaborating with global engineering teams. The role offers a strong platform for growth, allowing you to influence architecture decisions, improve operational excellence, and develop expertise in regulated financial data environments. Your key responsibilities as a DevOps Engineer will include: - Owning and evolving CI/CD pipelines using GitHub Actions and/or Azure DevOps to support seamless, automated deployments - Automating build, test, packaging, and deployment processes for .NET applications, database projects, and front-end components - Managing release processes, including branching, versioning, and deployments across development, UAT, and production environments - Maintaining Infrastructure as Code for cloud and on-prem environments to ensure scalability and consistency - Building and maintaining monitoring, alerting, and logging solutions to ensure platform reliability and performance - Supporting incident response, root cause analysis, and continuous improvement of operational processes and runbooks Qualifications - Strong experience building and managing CI/CD pipelines using tools such as GitHub Actions or Azure DevOps - Solid expertise in scripting and automation using PowerShell and/or Bash - Hands-on experience working with cloud platforms such as Azure or AWS and implementing Infrastructure as Code - Experience with monitoring and observability tools such as Azure Monitor, Application Insights, Grafana, or ELK - Good understanding of networking, security best practices, and access control in cloud or enterprise environments

Lead the design and maintenance of CI/CD pipelines, automate application deployment workflows, manage cloud-based applications for scalability and reliability, and mentor team members on DevOps best practices and continuous improvement initiatives.