{"id":2016418,"url":"https://alion.io/job/objectware-ingenieur-ia-llmmlopslanggraph","title":"Ingénieur IA LLM/MLOps/LangGraph","company":{"id":172908,"name":"Objectware","domain":"objectware.fr","url":"https://alion.io/company/objectware","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"freelance","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["France"],"countries":["FR"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":43000,"max":93000,"currency":"EUR","period":"year","gross":null,"usd_annual":104650},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Model Distillation","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-09-22T13:44:01Z","employer_posted_date":null,"last_verified_at":"2026-09-22T13:44:01Z","board_verified":false,"closed_at":null,"days_open":21,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":21},"description":"Les missions sont:\n1- Conception et développement d’agents autonomes avec LangGraph :\n- Architecture des agents conversationnels ou décisionnels (ex: agents multi-outils, boucles de rétroaction, mémoire contextuelle).\n-Intégrtion des modèles de langage (LLMs) via LangGraph pour des tâches complexes (ex: raisonnement multi-étapes, orchestration de workflows).\n-Optimisation les stratégies de prompt engineering et les mécanismes de contrôle (ex: function calling, tool use, plan-and-execute).\n2- Intégration avec des pipelines ML/MLOps existants :\n-Connection des agents LangGraph à des modèles custom (ex: fine-tuned LLMs, modèles de séries temporelles) via APIs ou pipelines.\n-Automatisation de versioning des agents et leur déploiement.\n3- Ingénierie des données et features pour les agents :\n-Construction des pipelines de données pour alimenter les agents\n-Développement des mécanismes de mémoire (ex: bases de connaissances dynamiques).\n-Optimisation les coûts et la scalabilité (ex: caching des réponses, model distillation pour les edge cases).\n4- Tests et robustesse :\n-Mise en place des tests automatisés pour les agents (ex: simulations de dialogues, injection de fautes).\n-Surveillance les dérives comportementales (ex: hallucinations, boucles infinies) avec LangSmith.\n-Documentation des limites et cas d’usage (ex: matrice de risques pour les décisions critiques).\nCompétences souhaitées:\nExpérience avancée avec LangGraph :\n-Maîtrise des composants clés : StateGraph, prebuilt tools, human-in-the-loop, et intégration avec des LLMs.\n-Expérience en déploiement d’agents en production.\nPython et écosystème IA :\n-Bibliothèques : langgraph, langchain, llama-index, pydantic.\n-Frameworks ML : PyTorch/TensorFlow (pour customiser des modèles si besoin).\n\nProfil recherché\nLes missions sont:\n1- Conception et développement d’agents autonomes avec LangGraph :\n- Architecture des agents conversationnels ou décisionnels (ex: agents multi-outils, boucles de rétroaction, mémoire contextuelle).\n-Intégrtion des modèles de langage (LLMs) via LangGraph pour des tâches complexes (ex: raisonnement multi-étapes, orchestration de workflows).\n-Optimisation les stratégies de prompt engineering et les mécanismes de contrôle (ex: function calling, tool use, plan-and-execute).\n2- Intégration avec des pipelines ML/MLOps existants :\n-Connection des agents LangGraph à des modèles custom (ex: fine-tuned LLMs, modèles de séries temporelles) via APIs ou pipelines.\n-Automatisation de versioning des agents et leur déploiement.\n3- Ingénierie des données et features pour les agents :\n-Construction des pipelines de données pour alimenter les agents\n-Développement des mécanismes de mémoire (ex: bases de connaissances dynamiques).\n-Optimisation les coûts et la scalabilité (ex: caching des réponses, model distillation pour les edge cases).\n4- Tests et robustesse :\n-Mise en place des tests automatisés pour les agents (ex: simulations de dialogues, injection de fautes).\n-Surveillance les dérives comportementales (ex: hallucinations, boucles infinies) avec LangSmith.\n-Documentation des limites et cas d’usage (ex: matrice de risques pour les décisions critiques).\nCompétences souhaitées:\nExpérience avancée avec LangGraph :\n-Maîtrise des composants clés : StateGraph, prebuilt tools, human-in-the-loop, et intégration avec des LLMs.\n-Expérience en déploiement d’agents en production.\nPython et écosystème IA :\n-Bibliothèques : langgraph, langchain, llama-index, pydantic.\n-Frameworks ML : PyTorch/TensorFlow (pour customiser des modèles si besoin).","description_format":"text","description_chars":3547,"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":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Custom Software Development","Data Engineering & Migration 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