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Vous contribuerez à la conception, au développement et à l'optimisation d'une architecture d'IA générative robuste, sécurisée et entièrement déployée en environnement on-premise.\nResponsabilités du poste\nConcevoir, développer et maintenir des services backend Python destinés à des applications d'IA générative en production.\n\nDévelopper et orchestrer des workflows agentiques complexes reposant sur des frameworks tels que LangGraph, CrewAI, BeeAI ou AG2.\n\nMettre en œuvre des architectures RAG performantes intégrant outils, function calling et collaborations multi-agents.\n\nConcevoir et optimiser les pipelines d'ingestion documentaire, d'OCR et de structuration de contenus issus de documents numérisés.\n\nDéployer et exploiter des modèles de langage open-weight hébergés localement, en garantissant performance, sécurité et disponibilité.\n\nDévelopper et faire évoluer des API performantes avec FastAPI et assurer leur déploiement dans des environnements Kubernetes.\n\nMettre en place les bonnes pratiques de CI/CD, d'observabilité, de gestion des secrets et de protection des données sensibles.\n\nParticiper aux choix techniques et architecturaux afin d'assurer la scalabilité, la maintenabilité et la maîtrise des coûts de la solution.\n\nRequirements\nSolide expérience en développement backend avec Python 3.11+ dans des environnements de production, incluant typage, tests automatisés (Pytest) et bonnes pratiques de développement logiciel.\n\nExpérience concrète de mise en production de solutions IA/ML, au-delà de phases de démonstration ou de prototypage.\n\nMaîtrise d'au moins un framework d'orchestration agentique tel que LangGraph, CrewAI, BeeAI ou AG2.\n\nExpérience des bases de données vectorielles (Chroma, Qdrant, Weaviate ou équivalent) pour l'indexation et la recherche sémantique à grande échelle.\n\nConnaissances approfondies des technologies OCR et de traitement documentaire, notamment Docling, PaddleOCR ou solutions comparables.\n\nExpertise dans le déploiement et l'exploitation de LLM auto-hébergés avec des solutions telles que llama.cpp, vLLM ou équivalent.\n\nBonne maîtrise des API REST, de FastAPI, des conteneurs Docker et des environnements Kubernetes.\n\nExpérience en fine-tuning ou déploiement de modèles open-weight tels que Mistral, Llama ou Apertus.\n\nCapacité à prendre des décisions d'architecture en tenant compte des enjeux de performance, qualité, sécurité, exploitabilité et coûts.\n\nParfaite maitrise du français.","description_format":"text","description_chars":2580,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"French","level":"Advanced (C1)","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Outsourcing & Dedicated Teams","Custom Software 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