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# Architecte - Tech Lead Azure - Platform Engineering & MLOps H/F

- **Company:** Propertyvalue
- **Location:** France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Cloud Computing, Continuous Integration, DevOps, Python (Programming Language), Machine Learning, Windows PowerShell, Azure Machine Learning, Azure Data Factory, Delivery Pipeline, Kubernetes, Machine Learning Operations, Terraform, Docker
- **Published:** September 9, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/83159896.html

## About the Role

5+ ans d'expérience sur des environnements Cloud / DevOps / Platform Engineering.

 - Très bonne maîtrise de Microsoft Azure.
 - Expérience significative avec Azure Machine Learning et Azure DevOps.
 - Solide maîtrise de Terraform / Infrastructure as Code.
 - Expertise des environnements Kubernetes / containers.
 - Bonne compréhension des pratiques MLOps et SRE.
 - Capacité à intervenir comme référent technique / Tech Lead, tout en restant hands-on.
 - Bon niveau de communication et capacité à travailler avec des équipes internationales., Microsoft Azure
Machine learning
PowerShell
Kubernetes
Python
Terraform
Monitoring
Docker

## Description

Dans le cadre du développement d'une plateforme Data & IA stratégique, nous recherchons un Architecte / Tech Lead Azure expérimenté pour intervenir sur des enjeux de Platform Engineering, DevOps/SRE et MLOps.

La mission porte sur la conception, l'industrialisation et la fiabilisation d'une plateforme de développement Machine Learning basée sur Microsoft Azure, avec Azure Machine Learning et Azure DevOps au coeur de l'environnement., Concevoir et faire évoluer l'architecture de la plateforme Azure Data & IA.

 - Déployer et maintenir des infrastructures cloud robustes, scalables et sécurisées.
 - Concevoir et industrialiser les pipelines CI/CD avec Azure DevOps.
 - Automatiser le provisioning via Infrastructure as Code (Terraform).
 - Administrer et optimiser les environnements containerisés et Kubernetes / AKS.
 - Industrialiser le cycle de vie des modèles et les pratiques MLOps.
 - Mettre en place le monitoring, les logs, le tracing et les dashboards d'observabilité.
 - Garantir la disponibilité, la performance, la sécurité et la fiabilité de la plateforme.
 - Accompagner les équipes Data Science et Data Engineering sur les bonnes pratiques DevOps/SRE.
 - Participer aux choix d'architecture et à l'évaluation de nouvelles technologies.

Environnement technique

Azure - Azure Machine Learning - Azure DevOps - AKS / Kubernetes - Docker - Terraform - Python - PowerShell - MLOps - MLflow - CI/CD - Monitoring / Observabilité - Networking


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