Design and implement machine learning models and algorithms.
Analyze large datasets to derive insights and improve predictive systems.
Build and deploy scalable ML solutions for real-time and batch processing.
Collaborate with product, engineering, and data teams to integrate ML models into production systems.
Continuously evaluate model performance and iterate as needed.

Strong knowledge of Python and libraries like NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch.
Experience with ML model development, training, validation, and deployment.
Understanding of data preprocessing, feature engineering, and model optimization.
Familiarity with SQL and working with large-scale datasets.
Good understanding of algorithms like regression, classification, clustering, etc.
Knowledge of version control (Git), and working in Agile environments.
Experience with REST APIs and ML integration with applications is a plus.

Experience with cloud platforms (AWS/GCP/Azure).
Exposure to MLOps tools and frameworks.
Understanding of recommendation engines, fraud detection, or personalization systems.