Architecte data & ai rabat salé - Kenitra

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.