ML Engineer

Overview

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 Tensor Flow, 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.