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# Tech Lead Data Science / MLOps H/F

- **Company:** Open
- **Location:** Levallois-Perret, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Cloud Computing, Continuous Integration, Data Governance, DevOps, Python (Programming Language), Machine Learning, Open Data Protocol, Tensorflow, SQL Databases, Pytorch, Large Language Models, Data Lakes, Scikit Learn, Kubernetes, Machine Learning Operations
- **Published:** May 28, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=054584e31df7651a

## About the Role

Data / Data Engineering : SQL, pipelines, orchestration (Airflow, DBT), Data Lake.

Data Science / ML : Python, MLflow, Scikit-learn, PyTorch/TensorFlow.

MLOps : MLflow, Kubeflow, CI/CD ML, monitoring (Evidently).

## Description

Si vous êtes passionné(e) par la Data Science, le MLOps et les architectures Cloud, curieux(se) des technologies de pointe (ML engineering, CI/CD, orchestration, monitoring des modèles) et motivé(e) par l'industrialisation de solutions IA à fort impact métier, cette opportunité est faite pour vous.

Au sein de notre Business Unit Open Data & IA, vous interviendrez auprès de clients Grands Comptes sur des projets stratégiques.

Votre mission principale est de :

 * Concevoir, développer et industrialiser des solutions Data & IA permettant de transformer des besoins métier en produits fiables, scalables et maintenables. Le rôle implique la mise en place de pipelines automatisés, l'intégration continue, le déploiement de modèles, et la collaboration étroite avec les équipes Data, DevOps et métier.
 * Développer et maintenir des pipelines de données et/ou de modèles (CI/CD, automatisation).
 * Construire, entraîner et optimiser des modèles de Machine Learning ou LLM.
 * Industrialiser des POC en produits robustes (monitoring, versionning, tests).
 * Gérer l'infrastructure : conteneurs, orchestration, cloud, sécurité.
 * Assurer la qualité et la gouvernance des données.
 * Collaborer avec les équipes techniques et les métiers pour prioriser les évolutions.


## Related Videos

- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
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- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
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- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)