Machine Learning Engineers require a strong mix of technical and analytical skills to build effective AI solutions. Proficiency in Python programming and familiarity with libraries such as NumPy, Pandas, and Scikit-learn are essential. A solid understanding of statistics, probability, and linear algebra helps in developing and optimizing models. Knowledge of machine learning algorithms, feature engineering, and model evaluation is crucial for accuracy and performance. Additionally, skills in data preprocessing, problem-solving, and critical thinking are important. Familiarity with deep learning frameworks, cloud platforms, and deployment tools further enhances an engineer’s ability to deliver real-world machine learning applications successfully.