Leonardo Ribeiro

I’m an AI/Machine Learning Engineer with a “robust background in Big Data Architecture and Data Science,” specializing in productionizing Generative AI and Large Language Models (LLMs). My work centers on “design, implement, and provide solutions for Machine Learning projects that deliver value and impact,” from anomaly detection and recommender systems to fraud detection, text classification, and time series analysis.

At Natual AI, I enhance generative AI solutions with AWS Knowledge Bases and AWS Bedrock, and I build “end-to-end MLOps pipelines” that cover optimization systems and LLM-powered conversational agents. In my recent Stellantis role, I implemented a feature store with AWS and Databricks, provisioned and monitored graph database environments with Neo4j, and guided Data Scientists on MLOps adoption using Databricks MLFlow across Neural Networks, XGBoost, Regression, Clustering, and Classification. I’m also certified as a Google Cloud Professional Machine Learning Engineer, and I bring a strong MLOps ethos—feature engineering, model serving strategies, versioning, monitoring, and drift detection—backed by hands-on experience with Airflow, CI/CD via GitHub Actions, and scalable data processing integrating BigQuery, Cassandra, and MongoDB.