Guillaume RAKOTONJANAHARY Tsantaniaina Amboarasoa

Developed medical RAG pipelines, automated BI assistants and Document AI solutions with fine-tuned LLMs and OCR, improving clinical reporting and decision support for health stakeholders.

Designed and deployed end-to-end AI and data platforms including OLTP→BigQuery OLAP migrations, CDC pipelines, and forecasting/anomaly detection models, improving BI responsiveness and enabling real-time biometric KPI monitoring.

Delivered hybrid RAG architectures, large-scale ETL/ELT pipelines and vector+graph search integrations, enabling automated BI assistants and production ML deployments with Vertex AI and BigQuery ML.

Built data ingestion and ML-enabled bulletin modules with multi-source ETL (SQL Server, DHIS2), LangChain orchestration and fine-tuned GPT models to automate epidemiological dashboards and insights.

Led development of multi-tenant healthcare SaaS with offline-first React Native clients, CDC pipelines (PostgreSQL→BigQuery), and real-time vaccine stock tracking, reducing sync costs by ~60%.

Activities and societies: Thesis project on hybrid RAG architecture, clustering algorithms, image processing pipelines, Apache Spark/DataProc

Master (MSc) in Big Data and Artificial Intelligence focused on distributed architectures, Apache Spark/DataProc, and image processing, completed December 2022 to December 2024.

Activities and societies: Modular architecture projects, UML methodology, reusable design patterns, technical documentation

Bachelor's Degree in Computer Science with specialization in Development completed between December 2019 and December 2022, covering software engineering, data systems, and applied AI techniques.

Scientific Baccalaureate (Series C) completed in October 2019 with focus on scientific and technical foundations and UML methodology for design.