Python Developer, AI

RGI S.P.A.

. Design RAG and agentic solutions for concrete business use cases .

Posted 11/11/2026full-timeParis • FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI solutions using Python, with a strong focus on API development, generative AI, and agentic architectures. Proficient in managing diverse document sources and ensuring solution observability and quality.

Highest-signal resume keywords

Proficiency In Python 3.11+API Development With FastAPIExperience With Generative AI SolutionsKnowledge Of RAG And Hybrid SearchStrong SQL Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingAPI DevelopmentGenerative AI SolutionsRAG KnowledgeHybrid Search ImplementationSQL ProficiencyDocument IngestionPrompt EngineeringTesting With PytestDependency Management

Tools & Technologies

FastAPILangChainLangGraphPostgreSQLS3 Object StorageMLflowGitGitLab CIDockerKubernetes

Certifications & Qualifications

Master’s Degree In Computer Science

Industry Keywords

Agentic ArchitecturesAI Solution ObservabilityDocument SourcesChunkingRetrievalEmbeddingStreaming ResponsesRole ManagementSecurity ConsiderationsQuality Monitoring

Tech Stack

Tools & technologies

DockerKubernetesPostgresPythonSQLTypeScript

About the role

Key responsibilities & impact

Design RAG and agentic solutions for concrete business use cases
Develop and orchestrate agents while ensuring source traceability and minimizing hallucinations
Contribute to the KELIA product configuration workflow, with human validation at key stages
Help deploy assistants to production, from their APIs through integration into the internal web interface
Develop AI features in Python across the entire lifecycle, from design through production deployment
Extract text and tables from PDFs, Confluence pages, SharePoint documents, and GitLab repositories
Chunk, index, and update content using asynchronous jobs
Implement hybrid semantic and keyword search with filters, reranking, and visual search
Develop LangGraph agents for routing, search, filtering, reasoning, and response generation
Write and version prompts, cite sources, and indicate when information cannot be found
Develop the agent that extracts and configures general terms and conditions to produce a file that can be imported into KELIA
Maintain test datasets, measure answer accuracy, and track hallucinated responses
Verify result stability from one run to the next
Monitor token consumption, time to first response, and caching, and select the appropriate model for each task
Expose assistants through APIs with streaming responses and integrate them into the internal web interface
Document code, write tests, and participate in code reviews

Requirements

What you’ll need

Master’s degree (Bac+5 equivalent) in computer science or insurance
Proficiency in Python 3.11+
Proficiency in API development with FastAPI, Pydantic, and SSE streaming
Experience with or a strong interest in generative AI solutions, LLMs, and agentic architectures using LangChain, LangGraph, and LiteLLM
Knowledge of RAG, hybrid search, embeddings, and reranking principles, ideally with Qdrant
Experience ingesting diverse document sources, including PDFs, Confluence, SharePoint, and GitLab, using tools such as Docling
Proficiency with storage and persistence environments based on PostgreSQL and S3 object storage
Awareness of AI solution observability and quality considerations, including monitoring prompts, traces, logs, costs, and performance, using tools such as MLflow
Awareness of security, authentication, and role-management considerations, particularly with Keycloak
Experience with industrialized development environments using Git, GitLab CI, Docker, Kubernetes, Helm, and pytest
Proficiency with the collaborative tools Jira and Confluence
Strong command of Python best practices, including pytest testing, typing, and dependency management
Experience using LLM APIs and knowledge of prompt engineering fundamentals, including structured outputs
Understanding of how RAG works, including embeddings, chunking, retrieval, and generation
Strong SQL skills and basic database knowledge
Proficiency with Git and the ability to work effectively as part of a team

Benefits

Comp & perks

Permanent, full-time employment
Hybrid work arrangement in Paris or Lille
Work with cutting-edge AI technologies and solutions integrated into core business tools
Real-world generative AI, RAG, and LLM agent use cases, from experimentation through industrialization
Domain expertise in the insurance sector and the opportunity to create solutions with real-world impact