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Développeur IA — RAG & Guardrails
CAT-AMANIA
This listing is optimized for AI-native talent : clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
3 skills 2 tools
Workplace Hybrid
Location 32 Avenue de Wagram, 75008 Paris, France · Hybrid
Compensation EUR 500-650/day Benefits & perks Mission longue, télétravail partiel, équipe plateforme. How to apply Montre une implémentation guardrails avec logs et policy tests.
Listed Sep 24, 2026 CAT-AMANIA is hiring a Senior Développeur IA — RAG & Guardrails on the RAG Security team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI —not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
CAT-AMANIA ouvre une mission RAG + guardrails avec forte exigence de gouvernance et suivi opérationnel.
Le poste couvre retrieval, politiques de sécurité, tests de robustesse et monitoring continu.
Ce que vous allez faire
Livrer en production des systèmes RAG avec guardrails en production avec évaluation offline, monitoring et plan de rollback. Travailler avec produit, métier et design pour cadrer les besoins et priorités. Améliorer qualité des données, observabilité et KPI business liés aux usages IA. Documenter runbooks, incidents et standards de sécurité pour l'équipe. Profil recherché
Expérience concrète Python / stack ML-LLM avec features déjà déployées. Bonne pratique SQL, expérimentation et communication claire aux non-tech. Capacité à gérer les compromis qualité, coût, latence et sécurité. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
NLP
Text pipelines: tokenization, fine-tuning, evaluation, and guardrails for user-facing language features.
Vector DBs Tool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.
Docker Tool
Containerized services and reproducible dev environments for ML and LLM workloads.
✓ You can show evidence in your application: Montre une implémentation guardrails avec logs et policy tests. ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, NLP, Vector DBs, and Docker. ✓ You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals. ✓ You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote. ✓ You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords. ✓ You read the full listing (comp: EUR 500-650/day, benefits in At a glance) and your expectations align. What the employer asked for
Montre une implémentation guardrails avec logs et policy tests.
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Développeur IA — RAG & Guardrails
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