Maxime B.

Data Engineer Consultant - Modeo (RATP Mission)

Design and deployment of modern data platforms for industrialization, centralization, and valorization of transport data. Involvement in agile teams of 2 to 15 developers.

Key achievements:

Architecture and Modeling: Building the Data infrastructure on the Modern Data Stack (MDS). Design of data pipelines and modeling of analytics-oriented databases.

ETL/ELT Pipelines with dbt and Snowflake: Development of complex data transformation flows with dbt and optimized storage on Snowflake. Automation of refreshes and improvement of computation performance.

Orchestration with Airflow: Deployment and management of DAGs (Directed Acyclic Graphs) under Apache Airflow for scheduling, monitoring, and securing critical flow execution.

Industrialization and Automation: Creation of a flow automation framework to standardize the integration of new data sources, thereby reducing development time and cloud infrastructure costs.

Software Industrialization: Management of versioning, integration, and continuous deployment via Gitlab CI/CD pipelines to ensure the stability of production environments.

Technical Environment: Python, SQL, Snowflake, Apache Airflow, dbt, Gitlab CI/CD.

AI Engineer at Capgemini

AI Engineer specializing in generative AI, I support companies in the design and implementation of innovative solutions based on LLM models and automated pipelines. My role includes defining architectures, technical integration, and ensuring the quality of deliverables.

Mission 1: Gen AI Engineer - Automation of control report generation with generative AI

Design and implementation of a data processing flow integrating analysis based on LLM models.

Support clients in their technical and strategic choices related to the project.

Development of structuring diagrams to define data flow and transformation.

Management and orchestration of different LLM models for analysis and content generation.

Implementation of validation metrics and hallucination control mechanisms to ensure generation quality and quantify project progress.

Technical Environment: Python, VM, JIRA, On-Premise hosting

Mission 2: Gen AI Engineer - Generation of personalized monograms for VivaTech 2025 trade show via generative AI

Monitoring client requests and coordinating with technical teams to ensure compliance with specifications.

Automation of generating a large volume of images and managing load.

Implementation of a quality control system and integration of specifications into pipeline development.

Addition of a content generation control mechanism to ensure consistency and compliance.

Technical Environment: Python, CVAT, Docker, Azure

Structuring internal offers around data governance, data engineering, and generative AI.

End-to-end data project management: scoping, steering, team coordination, deliverable tracking, and client relations for the public sector.

Supporting clients in defining their data strategy and implementing solutions tailored to their business challenges.

Facilitating workshops, formalizing needs, writing functional and technical documentation.

Monitoring data governance and the application of the AI Act to businesses.