Khaled Hechmi is a Senior Machine Learning Engineer with 8 years of experience building production-ready AI systems that bridge research and real-world impact. He has led end-to-end ML projects from problem scoping to deployment and monitoring, shipping solutions on platforms like AWS (Sagemaker, Fargate), Azure DevOps and edge devices such as Raspberry Pi and Nvidia Jetson. His recent work includes CNN-based sensor models and robust drift-detection pipelines for environmental sensing, and earlier projects delivered tangible business gains—45% AWS cost savings and a 20% lift in conversions for a news recommender, plus an 8% MAPE improvement in house-price estimates. Comfortable mentoring teams, he promotes MLOps best practices (MLflow, model registries, CI/CD, TDD) and has hands-on experience with PySpark, TensorFlow/PyTorch and data-versioning. Based in Milan with an MSc in Data Science, he combines a computer-science foundation with applied R&D instincts and a knack for translating complex research questions into operational products. An understated strength is his focus on geographically-aware monitoring and clustering to catch localized model anomalies before they affect users.
8 years of coding experience
5 years of employment as a software developer
Summer School Machine Learning for speech, Summer School Machine Learning for speech at University of Eastern Finland
Master of Science - MS Data Science, Master of Science - MS Data Science at Università degli Studi di Milano-Bicocca
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Khaled Hechmi - Senior Machine Learning Engineer at EcoVadis