Machine Learning Enginee

Role Description The Machine Learning Engineer will design, develop, and deploy machine learning models to support data-driven products and services. Day-to-day responsibilities include building and optimizing algorithms, training and evaluating neural network models, and implementing pattern recognition solutions for real-world datasets. The role involves collaborating with cross-functional teams to define requirements, translating business problems into technical solutions, and maintaining scalable, reliable ML pipelines in production environments. The engineer will also document model behavior, monitor performance, and iterate on solutions based on feedback and new data. This is a part-time, remote role that requires effective communication, self-management, and the ability to work across time zones.

Qualifications

Strong foundation in Computer Science and Algorithms, with experience applying them to machine learning problems. Proficiency in Pattern Recognition and Neural Networks, including model architecture selection and training techniques. Solid understanding of Statistics for data analysis, model evaluation, and performance metrics. Experience with programming languages commonly used in ML (e.g., Python) and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Background in data processing and feature engineering using large and complex datasets. Ability to write clean, maintainable code and collaborate using version control tools (e.g., Git). Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience. Strong problem-solving skills, attention to detail, and ability to work independently in a remote, part-time environment.