Architecte Data & AI

Background and Mission Objectives:

As part of AXA's platform modernization and unification initiative, we are seeking a Data & AI Architect to strengthen the technical governance and architecture of our Data & AI platform. The mission aims to accelerate the industrialization of AI and ML use cases, ensure coherence across MLOps/LLMOps environments, and support the maturity growth of DataOps and Delivery teams.

Key Responsibilities:

Architecture and Technical Governance:

• Contribute to the evolution of the Data & AI foundation services in alignment with AXA France’s Agentic AI strategy.
• Design and formalize target architectures for AI/LLM use cases (MLOps, LLMOps, DataOps, Unity Catalog).
• Oversee the implementation of Unity Catalog across all Databricks environments (security, lineage, data sharing, FinOps).
• Define and maintain the AI foundation architecture framework, including governance, security, traceability, and observability principles.
• Ensure technical consistency between Data Factory, AI Factory, and operational systems.

Team Support & Leadership:

• Guide and support DataOps / MLOps teams in implementing best practices for automation, CI/CD, and model monitoring.
• Facilitate knowledge transfer and upskilling on Databricks, Unity Catalog, MLFlow, Azure ML, and Azure Data Factory.
• Collaborate with Delivery teams to ensure pipeline reliability and environmental resilience.

Innovation & Modernization:

• Prepare the platform to expose federated Data Products within the Agentic AI ecosystem (MCP interoperability, Managed Server, automation).
• Define and deploy an “AI by Design” approach, integrating compliance principles (GDPR, AI Act, ISO 42001).
• Propose architecture solutions for integrating internal LLM models (AXA Secure GPT, Mistral, Claude, etc.) and hybrid models.
• Identify and lead innovation initiatives (AI Observability, AI Cost Control / FinOps, Real-Time Data Activation).

Expected Deliverables:

• Unified Data & AI target architecture diagram.
• MLOps/LLMOps implementation framework (CI/CD, MLFlow, observability).
• Best practices guide for DataOps & Knowledge Transfer.
• Performance and maturity tracking dashboard for AI/ML.
• Recommendations for evolving Data & AI governance (federation, autonomy, security).

Technical Environment:

• Azure Cloud: Data Factory, Databricks, Data Lake Storage, Event Hub, Key Vault.
• Databricks Platform: Unity Catalog, Delta Sharing, MLFlow, Structured Streaming, AutoML.
• Azure ML / AI Gateway: orchestration, model deployment, automation.
• CI/CD & DevOps: Terraform, Azure DevOps.
• Programming Languages: Python, Spark, PySpark.

• Master’s degree in Computer Science, Data, or AI (or equivalent).
• Minimum 7 years of proven experience in Data & AI architecture.
• Expertise in Databricks Lakehouse Platform (Unity Catalog, MLFlow, Delta Live Tables, Delta Sharing).
• Strong knowledge of Azure Data & AI environments, MLOps, LLMOps, and modern AI frameworks.
• Solid understanding of FinOps, security, data lineage, and observability best practices.
• Excellent interpersonal skills and ability to work in an agile, collaborative environment.