Design, develop, and implement machine learning models for various applications such as classification, regression, clustering, and recommendation systems.
Clean, preprocess, and transform large datasets; work with structured and unstructured data from diverse sources.
Choose appropriate algorithms based on business goals, and optimize models for performance, scalability, and accuracy.
Deploy models into production environments and integrate with existing systems via APIs or pipelines using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
Monitor model performance post-deployment and retrain/update models as needed to maintain accuracy and relevance.
Work closely with data scientists, software engineers, product managers, and stakeholders to define project goals and deliverables.
Stay up to date with the latest ML/AI research and incorporate cutting-edge techniques and frameworks when applicable.
Utilize ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, XGBoost, and others for model building and experimentation.
Ensure machine learning solutions are scalable and optimized for performance on large datasets or real-time systems.
Maintain clear documentation of model development, data workflows, and experiments for reproducibility and future reference.
Adhere to data privacy laws, model explainability standards, and ethical AI practices in all stages of ML development.
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