Summary
Armel Soubeiga is a Machine Learning and GenAI Research Engineer based in Paris with eight years of experience building production-grade AI systems that bridge research and deployment. He designs multimodal LLM agents, RAG and knowledge-graph pipelines, and uncertainty-aware neural models—combining fine-tuning, PromptNER, Neo4j visualization and LangChain-AWS deployments. His background spans unsupervised evidential clustering for uncertain time series, interpretable hybrid models for time-series explainability, and practical BI and visualization stacks (PowerBI, RShiny, Flask) from prior health-data and academic projects. A PhD candidate in Computer Science and Machine Learning with teaching roles in statistics and big data, he pairs deep research on evidential methods with hands-on engineering across R, Python, Spark and cloud tooling. Notably, he authored a practical book on web scraping with R and has applied his techniques to both healthcare trajectories and multimodal document/image ingestion pipelines.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD) Computer Science and Machine Learning, Doctor of Philosophy (PhD) Computer Science and Machine Learning at Université Clermont Auvergne
Master 2 (M2) Master's degree Statistics and Data Science, Master 2 (M2) Master's degree Statistics and Data Science at Université Grenoble Alpes
Licence Statistics and Computer Science, Licence Statistics and Computer Science at Université Nazi Boni
English, French