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# Tech Lead AI Engineer H/F

- **Company:** Coexya France
- **Location:** Guyancourt, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Python (Programming Language), Management of Software Versions, Google Cloud, ReactJS, Retrieval-Augmented Generation, Large Language Models, Machine Learning Operations, Api Management
- **Published:** September 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b5483244bef9dc56

## About the Role

* Bac+5, au minimum un an d?expérience sur des systèmes agentiques déployés en production (au-delà du POC)

 * Maîtrise avancée de Python et de l'écosystème LangGraph/LangChain ; Java et React appréciés

 * Pratique LLMOps et observabilité LLM

 * AI Gateway / API Management; Azure (certification appréciée), AWS

 * Posture de lead : pédagogie, animation d'ateliers, anglais professionnel

## Description

Votre rôle sera de travailler à la réalisation de solution Agentique et l?exploration de solution IA pour répondre à des problématiques métiers. Il est attendu du profil une autonomie sur ses projets à adresser et la capacité à interagir en direct avec des équipes métiers.

Contexte : plateforme IA mutualisée multi-cloud (Azure, AWS, GCP), pilotée par une AI Gateway et une couche agentique LangGraph/LangChain, sous exigences de souveraineté et de conformité (RGPD, EU AI Act, Cloud Act)., * Ingénierie et architecture agentique : conception et mise en production de systèmes multi-agents en Python (LangGraph/LangChain : gestion d'état, cycles de réflexion, RAG), standardisation MCP et A2A

 * Gouvernance agentique : cadre de conception et de validation des agents (niveaux d'autonomie, human-in-the-loop, guardrails, identités machines Entra ID / OBO / SPIFFE, traçabilité EU AI Act)

 * LLMOps & observabilité : pipelines d'évaluation, versioning des prompts et modèles, traces et métriques LLM (Langfuse, OpenTelemetry), détection de dérive qualité/coûts, FinOps


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