Role overview

Senior DevOps Engineer (Full Remote from France)

Requirements and responsibilities

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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, 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.

What We're Looking For

  • 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.

Nice to haves

  • Jupyter Notebooks, broader ML/AI ecosystem
  • Data pipelines (Airflow, Dataflow, Kestra)
  • Redis clusters and infrastructure performance optimization

🗓️ 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)

What brings us together?

  • Opening perspectives - We are building bridges between different sides of the spectrum. We believe in freedom of speech, inclusivity, and a mix of cultures.
  • Building a safer world - Empathy is the key to understanding alterity. Caring for each other is our driver to creating a safe community.
  • Making bold moves - We are ambitious challengers. We are making against-the-flow choices to make visible changes in the platform's ecosystem
  • Creating meaning - We spark joy and curiosity, we feel good about what we do and we want everyone else to feel the same fulfillment
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Browse stack
FocusDevOps EngineeringRole area
Seniority signalSeniorCandidate level
StackAWS, CI/CD, DockerPrimary skills
Location1 accepted countryEligibility

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