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LightOn
Permanent (CDI) Full-time Senior GenAI platform Hybrid 2 Rue de la Bourse, Paris, IDF 75002, France · Hybrid Paris, France EUR 70k–95k/year + BSPCE 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.
Employment
Workplace Hybrid
Location 2 Rue de la Bourse, Paris, IDF 75002, France · Hybrid
Compensation EUR 70k–95k/year + BSPCE Benefits & perks BSPCE, remote partiel, modèles souverains. How to apply Pipeline RAG en prod avec evals et fine-tuning documentés.
Listed Jun 30, 2026
Updated Aug 3, 2026 LightOn is hiring a Senior Ingénieur LLM — RAG enterprise on the GenAI platform 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.
LightOn déploie des LLM souverains et des systèmes RAG enterprise pour des grands comptes européens depuis Paris .
Ingénieur LLM RAG (hybride Paris 2e) : retrieval hybride, fine-tuning, évaluation qualité et mise en production sur modèles LightOn.
À propos de LightOn
Scale-up française spécialisée GenAI et hardware photonique. Clients enterprise en finance, industrie et secteur public. Culture produit avec exigence conformité et souveraineté des données. Missions
Concevoir pipelines RAG avec chunking, reranking et guardrails. Fine-tuner et benchmarker modèles sur corpus clients anonymisés. Accompagner les équipes client au handover et au monitoring. Stack & environnement
Python, LangChain, PyTorch, vector DBs, Kubernetes. Hybride Paris 2e ; français et anglais. Profil recherché
5+ ans LLM/RAG en production B2B. Éligibilité travail en France. 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.
OpenAI API
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
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: Pipeline RAG en prod avec evals et fine-tuning documentés. ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, PyTorch, and Vector DBs. ✓ 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 70k–95k/year + BSPCE, benefits in At a glance) and your expectations align. What the employer asked for
Pipeline RAG en prod avec evals et fine-tuning documentés.
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