DevOps / MLOps Engineer

Consort Group

Ingénieur DevOps / MLOps responsable de l’industrialisation et de la sécurisation de solutions IA. Collaborant sur des environnements cloud complexes pour assurer la performance des services IA.

Posted 6/13/2026full-timeStrasbourg • 🇫🇷 FranceMid-LevelSenior💰 €45,000 - €52,000 per yearWebsite

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Hard Skills

CI/CDMLOpsDevOpscontainerizationorchestrationPythonconfiguration managementapplication securitymonitoringobservability

Soft Skills

incident managementoperational maintenanceautomationdocumentation

Tools & Technologies

DockerKubernetesMicrosoft AzureAzure AI FoundryAzure OpenAIEntra IDKey VaultMLflowLangfusen8n

Industry Keywords

AI governancesecuritycomplianceauditabilityproduction AIindustrialized data platformsAI applicationsdata workflowsAI workloadsperformance

Tech Stack

Tools & technologies

AirflowAzureCloudDockerKubernetesPythonVault

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

4 to 7 years of experience in demanding DevOps/MLOps environments
Practical DevOps and MLOps practices applied to AI/GenAI 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

Benefits

Comp & perks

A thoughtful and human-centered onboarding
Close managerial proximity and support
Continuous access to training
Concrete commitments: inclusion, equality, solidarity
A comprehensive HR package: health insurance, meal vouchers, works council (CSE), supplementary insurance
A culture of feedback and meaningful projects