Required skills
- Machine Learning
- Artificial Intelligence
- Large Language Models (LLMs)
- RAG (Retrieval-Augmented Generation)
- Generative AI
- Python
- API Development
- Asynchronous Programming
- Testing
- Agent Development
- Tool Integration
Languages: French
Job description
Freelance AI Engineer specializing in Generative AI, RAG, and Agents for an insurance client in Paris. The role involves developing and improving RAG pipelines and autonomous business agents connected to the information system.
Project overview
Mission freelance – Generative AI Engineer: RAG & Agents (M/F)
Client: large insurance company, Paris
Project details
ASAP | Duration: 12 months renewable
Frequency: 2 to 3 days on-site
according to profile
Experience: 3 years minimum in ML/AI, including 1 to 2 years on LLMs in production
Context
The AI department is developing business assistants and agents for claims management, underwriting, and customer support. Several RAGs are already in production, and the current challenge is twofold: improving their quality and moving to autonomous agents connected to the information system.
Missions
RAG
Agents
Quality and production
Technical Stack
Profile sought
Essential
Appreciated
- Design advanced RAG pipelines: parsing complex documents (PDFs, tables, scans), chunking strategies, hybrid search (vector + BM25), reranking
- Implement agentic RAG: query reformulation, routing between sources, multi-step search
- Reduce hallucinations and improve source citation reliability
- Develop business agents with tool calls: internal APIs, databases, document generation
- Design multi-agent architectures and workflows with human validation
- Expose IS tools and data via the MCP protocol
- Manage agent memory, state, and error recovery
- Build evaluation datasets and metrics: source fidelity, relevance, success rate of agent tasks
- Trace and debug agent executions
- Implement safeguards: prompt injections, personal data, agent scope of action
- Optimize latency and cost per query
- LLM: Azure OpenAI, Mistral, Claude
- Agent orchestration: LangGraph, MCP
- RAG: LlamaIndex, Docling, Azure AI Search, Qdrant, rerankers (Cohere, BGE)
- Evaluation and observability: Ragas, DeepEval, Langfuse
- Backend: Python, FastAPI, Pydantic
- Deployment: Docker, Kubernetes, GitLab CI
- At least one RAG in production with real users, and the ability to explain how its quality was measured and improved
- At least one agent with tool calls developed beyond the POC stage
- Solid Python backend skills (API, asynchronous, testing)
- GraphRAG or knowledge graphs
- Light fine-tuning (LoRA) or custom embeddings
- Insurance or banking sector
- Start: flexible
- Remote: hybrid
- Location: Paris, France, FR
- Languages: French
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