Senior DevOps Engineer (Full Remote from France)
WFA Digital Insight
The demand for experienced DevOps professionals with expertise in machine learning operations has skyrocketed, with a 25% increase in job postings over the past year. As companies like Dailymotion continue to invest in AI, the need for skilled engineers who can bridge the gap between ML teams and infrastructure has never been more pressing. With the rise of remote work, French-speaking candidates with a strong background in GCP, Kubernetes, and MLflow can capitalize on this trend. Before applying, candidates should be aware of the evolving landscape of MLOps and the importance of collaboration, scalability, and security in AI-first environments.
Job Description
About the Role
As a Senior DevOps Engineer at Dailymotion, you will be the linchpin between the ML team and the infrastructure, ensuring seamless integration and deployment of AI models. Your expertise will be crucial in empowering ML engineers with the tools and frameworks needed to iterate quickly and autonomously. You will work closely with cross-functional teams to accelerate time-to-market for production-ready ML products and own the ML CI/CD pipeline.The ML team at Dailymotion is moving rapidly, with over 100 active AI projects on GCP, and your mission will be to connect the dots, align best practices, and facilitate collaboration between ML engineers and the backbone team. You will be responsible for managing technical debt, laying the foundation for future projects, and acting as a technical mediator between teams.
What You Will Do
- Empower ML engineers with the necessary tools, infrastructure, and frameworks to iterate quickly and autonomously
- Accelerate time-to-market for production-ready ML products through seamless integration and proper service connections
- Own the ML CI/CD pipeline and adapt existing frameworks to meet ML-specific needs
- Enable large-scale ML experimentation and provide robust, reproducible, and scalable environments
- Deliver concrete MLOps building blocks, such as MLflow, Kubeflow, and KubeRay, and manage GPU infrastructure dynamically
- Tackle technical debt on existing projects while laying the groundwork for future initiatives
- Act as a technical mediator between ML and backbone teams, proposing solutions that benefit both sides
- Handle on-call responsibilities, including post-mortems and level-1 failure analysis
What We Are Looking For
- Solid background in MLOps or DevOps, with a focus on projects shipped to production
- Expertise in GCP, including Vertex AI, GKE, GCS, and BigQuery
- Full GitOps experience, with FluxCD as the primary tool and ArgoCD as an acceptable alternative
- Hands-on experience with Kubernetes in production environments, not just in a lab setting
- Familiarity with real MLOps tools like MLflow, Kubeflow, and KubeRay
- GPU-aware, with experience managing GPU scarcity during mass training runs
- Proficiency in Python, with Bash expected and Go or Rust as a plus
- Experience with IaC (Terraform), containerization (Docker, Helm), and observability (Prometheus, Datadog, Looker)
- Fluency in French and English
Nice to Have
- Experience with Jupyter Notebooks and the broader ML/AI ecosystem
- Knowledge of data pipelines (Airflow, Dataflow, Kestra)
- Familiarity with Redis clusters and infrastructure performance optimization
Benefits and Perks
- Opportunity to work with a leading AI-first company
- Collaborative and dynamic work environment
- Professional development and growth opportunities
- Flexible remote work arrangements
- Access to cutting-edge technologies and tools
- Competitive compensation package
- Comprehensive health insurance
- Generous paid time off and holidays
How to Stand Out
- Tip: Make sure your resume and cover letter highlight your experience with GCP, Kubernetes, and MLflow, as these are crucial skills for this role.
- Tip: Be prepared to discuss your approach to managing technical debt and implementing scalable solutions in a fast-paced environment.
- Tip: Familiarize yourself with Dailymotion's technology stack and be prepared to ask informed questions during the interview process.
- Tip: Showcase your ability to collaborate with cross-functional teams and communicate complex technical concepts to non-technical stakeholders.
- Tip: Highlight any experience you have with GPU infrastructure management and large-scale ML experimentation.
- Tip: Be prepared to discuss your experience with IaC, containerization, and observability, and how you have applied these skills in previous roles.
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