ResponsibilitiesDesign, develop, and deploy machine learning models for real-world business use casesPreprocess and analyze large datasets to extract actionable insightsCollaborate with data scientists, engineers, and stakeholders to define ML project scope and objectivesOptimize model performance and ensure scalability in production environmentsMaintain clear documentation and communicate findings to both technical and non-technical audiencesMonitor and troubleshoot model performance post-deploymentStay up-to-date with latest ML tools, trends, and best practicesRequirementsProven experience as a Machine Learning Engineer or similar roleStrong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, etc.Hands-on experience with data pipelines, model training, evaluation, and deploymentSolid understanding of statistics, data structures, and algorithmsFamiliarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)Experience with MLOps tools is a plus (e.g., MLflow, DVC, Airflow)Ability to work independently and manage time effectively in a hybrid work settingFluent in English; French or Dutch is a plusNice to HaveExperience with NLP, computer vision, or time series forecastingBackground in data engineering or software developmentPrevious work in finance, healthcare, or logistics sectors