Lead Data Scientist / Tech Lead GenAI & RAG – Dataiku, Azure AI Foundry & GCP

Project overview

Context and objectives of the mission

As part of a strategic program for the deployment and industrialization of Generative Artificial Intelligence solutions within a large banking group, we are looking for a Lead Data Scientist / Tech Lead GenAI & RAG.

You will work within a mature Data & AI environment on high-value projects, playing a central role in the design, industrialization, and evolution of RAG (Retrieval-Augmented Generation) solutions.

The mission requires dual expertise: genuine technical leadership in Data Science / Generative AI and operational mastery of Dataiku, Azure AI Foundry, and GCP environments.

You will work closely with the Data Science, Data Engineering, Architecture, Cloud, and Security teams to ensure the performance, reliability, and scalability of the deployed solutions.

Main missions

• Technical Leadership & Data Science Expertise
• Act as Lead Data Scientist and technical referent for GenAI and RAG solutions.
• Define technical directions, reference architectures, and best development practices.
• Support and challenge Data Science and Data Engineering teams in their technical choices.
• Conduct technical reviews and ensure the quality of developments.
• Structure reusable components, frameworks, and templates.
• Design and Development of GenAI / RAG Solutions
• Design, develop, and optimize robust and scalable RAG architectures.
• Lead strategies for embeddings, vectorization, and document indexing.
• Optimize retrieval, hybrid search, and reranking mechanisms.
• Define prompting standards and LLM workflow orchestration.
• Implement evaluation mechanisms for the relevance and quality of generated responses.
• Dataiku, Azure AI Foundry & GCP Expertise
• Design, develop, and industrialize AI workflows on Dataiku.
• Leverage Azure AI Foundry capabilities for Generative AI solutions and their orchestration.
• Provide technical expertise on Google Cloud Platform (GCP) services and architectures.
• Contribute to architectural and integration choices between different platforms.
• Ensure technical consistency, performance, and maintainability of solutions.
• Industrialization, LLMOps & Operations
• Define and implement LLMOps best practices.
• Industrialize document ingestion pipelines and RAG workflows.
• Develop CI/CD, monitoring, and observability practices.
• Optimize the performance, availability, and scalability of services.
• Supervise the lifecycle of models, embeddings, and vector indexes.
• Support the resolution of complex incidents and continuous improvement of the platform.
• Governance, Quality & Security
• Participate in defining technical standards and governance practices for GenAI.
• Ensure architectures comply with security and confidentiality requirements.
• Implement traceability, auditability, and quality control mechanisms.
• Contribute to risk management related to the use of LLMs in sensitive environments.

Project details

• Start: flexible
• Remote: hybrid
• Location: Paris, France, FR
• Languages: French