Machine Learning Specialist

Coding: Write clean, efficient, and well-documented Python code adhering to OOP principles (encapsulation, inheritance, polymorphism, abstraction). Experience with Python and related libraries (e.g., TensorFlow, PyTorch, Scikit-Learn). They are responsible for the entire ML pipeline, from data ingestion and preprocessing to model training, evaluation, and deployment End-to-End ML Application Development: Design, development, and deployment of machine learning models and intelligent systems into production environments, ensuring they are robust, scalable, and performant. Software Design & Architecture: Apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Contribute significantly to the architectural decisions for our ML platform and applications. Data Engineering for ML: Design and implement data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference. MLOps & Productionization: Establish and implement best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring (performance, drift, bias), and alerting systems for production ML models.

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