Enterprise Data & AI Architect – GenAI, Microsoft Fabric, Knowledge Graph & O

Project overview

Detailed description of the need

As part of a strategic Data & Artificial Intelligence transformation program, we are seeking a senior Enterprise Data & AI Architect to support a large international organization in designing and implementing its Data & AI architecture.

The mission is part of a Tech Innovation team, responsible for exploring, prototyping, and industrializing innovative technological solutions to address concrete business challenges.

The main objective: design a robust, reusable, and secure Data & AI foundation, enabling generative AI applications and intelligent agents to access, understand, and effectively leverage the company's data.

We are looking for an architect with in-depth expertise in modern Data environments (particularly Microsoft Fabric), AI architectures, and semantic approaches (Knowledge Graphs, ontologies), capable of defining a target vision while actively participating in its implementation.

Main missions

A particularly important focus of the mission.

• Definition of Data & AI Architecture
• Design the target Data & AI architecture and define an implementation roadmap adapted to the existing ecosystem.
• Architect shared capabilities enabling the discovery, access, preparation, integration, and contextualization of data for AI applications and agents.
• Define integration principles between Data platforms (Microsoft Fabric, Azure, Databricks…), AI services, APIs, and various data sources.
• Ensure the consistency, scalability, security, and reusability of the proposed solutions.
• Data Platforms Expertise & Data Source Integration
• Analyze the company's various data sources (structured, semi-structured, and unstructured) and their exposure methods to AI systems.
• Design architectures for data access, federation, integration, and preparation, particularly in Microsoft Fabric and Azure environments.
• Define strategies for metadata management, catalogs, semantic models, data quality, and Data Lineage.
• Arbitrate between different approaches: direct data access, APIs, pipelines, document search, or tools accessible to agents via MCP.
• Knowledge Graphs, Ontologies & Semantic Contextualization
• Design approaches for representing and contextualizing business knowledge using Knowledge Graphs (KG), ontologies, and semantic models.
• Structure the relationships between business entities, data sources, repositories, and business rules.
• Evaluate the contribution of knowledge graphs and ontologies to improving data understanding, search, and utilization by AI systems.
• Define mechanisms enabling AI agents to leverage a reliable, consistent, governed, and traceable business context.
• Study approaches combining Knowledge Graphs, semantic search, and RAG (GraphRAG in particular).
• Generative AI Architecture & Intelligent Agents
• Contribute to the design of architectures for GenAI applications and multi-agent / agentic systems.
• Define the methods for AI agents to access data and tools, particularly through APIs and the Model Context Protocol (MCP).
• Evaluate RAG approaches, hybrid search, contextual enrichment, and knowledge access orchestration.
• Consider the challenges of governance, confidentiality, access control, performance, costs, and response reliability.
• Support use cases from POC/MVP to the initial industrialization phases.
• Transverse Collaboration & Industrialization
• Collaborate closely with Data, AI, Enterprise Architecture, Cybersecurity, and business teams.
• Formalize architecture standards, principles, and recommendations.
• Challenge technological choices and evaluate their relevance against actual needs.
• Support initial deployments and capitalize on lessons learned to evolve the target architecture.
• Maintain active monitoring of Data & AI architectures, Knowledge Graphs, and agentic AI technologies.

Project details

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