Architect Cloud et Data H​/F

Job Title

Within the Direction de la Transformation Technologique, the Data & IA department aims to maximize the contribution of Data and Artificial Intelligence to the functioning of Crédit Agricole. It relies on the Chief Data Officer Groupe and the Data Lab Groupe & AI Factory Group, reference centers for the internal design of innovative and industrial Data & AI solutions in partnership with the Group's entities.

The Data Lab Groupe & AI Factory Group has all the Data competencies cooperating within multidisciplinary Squads according to an internal Agile-inspired method:

• Data & AI Engineering aimed at preparing data, defining architectures, infrastructures and packaging the solutions that will be deployed for integration into the Information System,
• Data Science Analytics and Semantics to design Artificial Intelligence algorithms based on open source exploiting structured (tabular) and unstructured data (text, image, voice, videos) to meet the needs expressed by the Group's business units,
• Project Management which with all partners and technical teams of the Data Lab Groupe, identifies and studies opportunities, frames projects and coordinates their implementation.

Reporting hierarchically to the Technical Manager of the Data&IA service, your missions (non-exhaustive) will be:
• Cloud Architecture & Infrastructure: o Design Cloud-Native architectures (AWS or GCP) meeting availability, performance, security and cost requirements, in line with business and IT issues. o Accompany projects in the choice, implementation and urbanization of cloud services, CI/CD, containerization and automation. o Ensure the consistency of architectures with Crédit Agricole Group standards and norms (urbanization, compliance, security, interoperability). o Integrate managed and/or containerized cloud services according to needs (EKS, ECS, Lambda, App Services…).

• Data Architecture & Platform o Define and evolve data architectures (Data Lake, Data Warehouse, Data Mesh) in line with the Group's Data strategy. o Design scalable and resilient data pipelines for ingestion, transformation and data availability (batch and streaming). o Select and integrate technologies suitable for use cases: relational and No

SQL databases, object storage solutions, orchestration tools (Airflow, Step Functions…). o Ensure data quality, traceability and governance throughout its lifecycle.

• AI Architecture & MLOps o Design architectures for the deployment and industrialization of Artificial Intelligence models (ML, NLP, Computer Vision). o Set up MLOps platforms pe

Remote work: occasional