DevOps / MLOps Engineer

Responsibilities

• You will be responsible for operating, industrializing and securing the AI platform and its ecosystem
• Implement and evolve AI- and MLOps-oriented CI/CD pipelines
• Industrialize the lifecycle of models, agents and AI applications
• Design and automate orchestration workflows (data & AI)
• Deploy and maintain containerized environments (Docker, Kubernetes)
• Integrate MLOps and observability tools into the production pipeline
• Strengthen configuration and secrets management mechanisms
• Contribute to establishing AI governance standards (security, compliance, auditability)
• Operate, maintain and evolve a production AI platform
• Ensure monitoring, observability and traceability of AI workloads
• Guarantee availability, performance and resilience of AI services
• Manage incidents, rollbacks and operational maintenance
• Support project and business teams in productionizing AI use cases
• Continuously automate and improve DevOps/MLOps operations
• Document architectures, pipelines and operational best practices

Qualifications

• 4 to 7 years of experience in demanding DevOps/MLOps environments
• Practical Dev

  Ops and MLOps practices applied to AI/Gen

  AI environments
• Cloud platform: Microsoft Azure (including Azure AI Foundry, Azure OpenAI, Entra ID, Key Vault)
• MLOps & tracking tools: MLflow, Langfuse
• Orchestration & automation: n8n, Airflow or equivalent
• Containerization & orchestration: Docker, Kubernetes
• CI/CD, configuration management, secrets management and application security
• Development and automation in Python
• Experience with productionized AI or industrialized data platforms