I am Leon Doungala, an Engineer specialized in AI/ML and Data Science based in Milan, Italy. With a Master of Science in Computer Engineering (AI specialization) and a Master's in Data Science, I specialize in developing innovative engineering solutions at the intersection of artificial intelligence and practical business applications.
As an AI/ML Engineer at People Lab (March 2024-Present), I've developed AI solutions for healthcare and corporate environments. My work includes creating a RAG-based Clinical Semantic Search Engine that processes over 160,000 unstructured medical referrals, and an advanced ATS system supporting HR recruiters across multiple companies. These systems leverage LangChain, Flask, Scikit-learn, PyTorch, and OpenAI's LLM APIs to deliver actionable insights.
Previously, as a Software Developer and Data Scientist at the Italian Stock Exchange (Euronext Group) (2022-2024), I collaborated with teams developing financial software applications, optimizing SQL DataMarts, and automating AWS data ingestion workflows. I contributed to data science initiatives applying machine learning models for risk assessment and financial forecasting, working within agile teams to deliver high-impact solutions.
My expertise spans predictive and generative AI, with particular focus on Large Language Models, Retrieval-Augmented Generation, Transfer Learning, and Vector Databases. I'm committed to leveraging these technologies to create scalable, efficient, and impactful AI systems that address real-world challenges, particularly in healthcare and financial domains.
"Artificial Intelligence and Machine Learning Techniques for Diabetes Prediction and Characterization" - Developing predictive models using ensemble learning methods to identify early diabetes markers and optimize personalized treatment pathways.
Graduated with honors, completing a cross-institutional research project on data-driven business optimization strategies implementing machine learning solutions for real-world business challenges.
"Design and Development of a Modern Cloud-Based Web Application in Java Enterprise Edition for Radio Streaming with Integrated Deep Learning Neural Networks" - Created an innovative streaming platform with artificial intelligence-powered content recommendation algorithms and personalized user experience.
My portfolio demonstrates expertise across the full AI/ML development lifecycle. In machine learning, I implement techniques ranging from traditional models (Random Forests, SVM, Decision Trees) to advanced approaches like Ensemble Learning with XGBoost and K-means clustering. These technical foundations enable me to develop sophisticated solutions for complex problems.
My M.Sc. thesis project applied AI and ML techniques to diabetes prediction and characterization, combining supervised models (XGBoost, Random Forest) with unsupervised techniques (K-Means, PCA) to extract actionable medical insights. I incorporated LangChain and Vector Databases for RAG workflows and semantic search, and leveraged generative AI for data augmentation to address imbalanced datasets.
In professional settings, I've built production-grade AI systems for semantic search, automation, and autonomous agents. Notable projects include a Virtual Healthcare AI Assistant providing medically-sound guidance based on symptoms, and a RAG-powered ATS System that aligns candidates with job descriptions using semantic search and conversational AI. Both systems demonstrate my ability to implement scalable, intelligent solutions for business-critical applications.
My CV details my journey as an Engineer specialized in AI/ML and Data Science, including my experience at People Lab, Euronext Group (Italian Stock Exchange), and my academic qualifications.
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