Responsibilities Design, develop, and deploy machine learning models for real-world business use cases Preprocess and analyze large datasets to extract actionable insights Collaborate with data scientists, engineers, and stakeholders to define ML project scope and objectives Optimize model performance and ensure scalability in production environments Maintain clear documentation and communicate findings to both technical and non-technical audiences Monitor and troubleshoot model performance post-deployment Stay up-to-date with latest ML tools, trends, and best practices Requirements Proven experience as a Machine Learning Engineer or similar role Strong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, etc. Hands-on experience with data pipelines, model training, evaluation, and deployment Solid understanding of statistics, data structures, and algorithms Familiarity 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 setting Fluent in English; French or Dutch is a plus Nice to Have Experience with NLP, computer vision, or time series forecasting Background in data engineering or software development Previous work in finance, healthcare, or logistics sectors