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# Machine Learning Engineer - IA Conversationnelle & Voicebot (h/f)

- **Company:** emagine Consulting SARL
- **Location:** Paris, France (Remote available)
- **Experience:** Experienced
- **Contract:** Apprenticeship
- **Skills:** Continuous Integration, Machine Learning, Solution Deployment Descriptor, Data Processing, Machine Learning Operations
- **Published:** July 18, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=afcb5e9c7f50cdc1

## About the Role

Compétences Requises : Minimum 3 ans d?expérience en Machine Learning / NLP.

Solide expérience en modèle NLP.

Compétences Souhaitables : Expérience avec des systèmes de voix (ASR / TTS).

Connaissance des pipelines MLOps.

## Description

Nous recherchons un ingénieur en apprentissage automatique orienté IA conversationnelle pour développer et déployer des solutions visant à améliorer l'expérience client dans le secteur de l'assurance.

RésuméLe poste de Machine Learning Engineer se concentre sur le développement et le déploiement de solutions d?IA conversationnelle.

Responsabilités : Recueillir et formaliser les besoins métiers (parcours vocaux, scénarios de conversation, KPIs).

Concevoir les architectures conversationnelles (voicebot, chatbot).

Entraîner, adapter et évaluer des modèles NLP.

Intégrer des briques voix (ASR / TTS) dans la chaîne conversationnelle.

Mettre en place les pipelines MLOps (préparation des données, entraînement, évaluation, CI/CD).

Assurer le suivi en run : monitoring qualité, dérive, optimisation continue.

Analyser les logs de conversations, identifier les axes d?amélioration.

Proposer et implémenter des améliorations de modèles, de prompts, de flux conversationnels.

Collaborer étroitement avec les équipes métiers, IT et relation client.


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Hello JARVIS - Building Voice Interfaces for Your LLMS](https://www.wearedevelopers.com/videos/1641-hello-jarvis-building-voice-interfaces-for-your-llms)
- [100 times more frequent deployments: How did we create a high performance team?](https://www.wearedevelopers.com/videos/1069-100-times-more-frequent-deployments-how-did-we-create-a-high-performance-team)
- [Implementing continuous delivery in a data processing pipeline](https://www.wearedevelopers.com/videos/73-implementing-continuous-delivery-in-a-data-processing-pipeline)
- [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down)
- [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects)

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