rb.

About Me

Software Engineer with a deep passion for Artificial Intelligence and MLOps. I focus on designing and developing robust, scalable systems, with current work centered on integrating advanced AI capabilities like Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) into demanding, real-world environments.

Education

Professional Experience

GE HealthCare

Software Engineer

Contributing to the development of a modular framework to accelerate the creation of medical applications, with a strong focus on image visualization (2D/3D) and integration of AI algorithms.

  • Developed core components to streamline image loading and enhance image interaction (2D/3D).
  • Integrated 3D rendering capabilities for advanced medical imaging use cases.
  • Contributed to the design and implementation of a Retrieval-Augmented Generation (RAG) system to power the framework’s documentation search.
  • Contributed to R&D on AI agents, synthetic dataset generation, and MCP use.
  • Collaborated with cross-functional teams (AI, UX, system architecture) to ensure robust and scalable solutions.

Airbus

Software Engineer

Delivered impactful software solutions by designing, developing, and deploying applications for a global digital platform.

  • Designed and integrated software solutions to enhance digital platform functionality.
  • Managed the full software lifecycle, from planning to deployment.
  • Conducted AI research and implementation to improve existing systems.

Ministry of Armed Forces Internship

Software Engineer / Data Scientist

Developed mission-critical applications integrating AI models, focusing on sensitive environments and high-security requirements.

  • Designed AI-powered applications for strategic environments.
  • Built MLOps pipelines for managing and deploying machine learning models.
  • Utilized ReactJS and FastAPI for high-performance user interfaces and secure backends.

Lab-STICC (UMR CNRS) Internship

Data Scientist

Internship focused on predicting wind speed and precipitation levels at sea based on underwater acoustic measurements. I leveraged the Copernicus ERA5 API to retrieve meteorological data and format it.

  • Automated retrieval of ERA5 weather variables (wind speed, precipitation) via the Copernicus Climate Data Store and alignment with timestamps of acoustic recordings.
  • Pre‑processing of acoustic signals: cleaning, normalization, spectral/statistical feature extraction.
  • Developed and trained machine learning and deep learning models to estimate wind speed and precipitation from acoustic features.
  • Evaluated model performance using metrics.