Machine Learning Engineer

As an experienced candidate with 7 to 15 years of experience, you will be responsible for the following key responsibilities: - Coding: You will be required to write clean, efficient, and well-documented Python code following OOP principles like encapsulation, inheritance, polymorphism, and abstraction. Additionally, you should have experience with Python and related libraries such as TensorFlow, PyTorch, and Scikit-Learn. - End-to-End ML Application Development: Your role will involve designing, developing, and deploying machine learning models and intelligent systems into production environments. It is crucial to ensure that these systems are robust, scalable, and performant. - Software Design & Architecture: You will apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Your contribution to the architectural decisions for the ML platform and applications will be significant. - Data Engineering for ML: Designing and implementing data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference will be part of your responsibilities. - MLOps & Productionization: You will 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. - Performance & Scalability: Identifying and resolving performance bottlenecks in ML systems, and ensuring the scalability and reliability of deployed models under varying load conditions will be essential. - Documentation: Creating clear and comprehensive documentation for ML models, pipelines, and services is a key part of this role. - Good to have Machine Learning Expertise: Having a solid theoretical and practical understanding of various machine learning algorithms, proficiency with ML frameworks like PyTorch, Scikit-learn, and experience with feature engineering, model evaluation metrics, and hyperparameter tuning will be advantageous. As an experienced candidate with 7 to 15 years of experience, you will be responsible for the following key responsibilities: - Coding: You will be required to write clean, efficient, and well-documented Python code following OOP principles like encapsulation, inheritance, polymorphism, and abstraction. Additionally, you should have experience with Python and related libraries such as TensorFlow, PyTorch, and Scikit-Learn. - End-to-End ML Application Development: Your role will involve designing, developing, and deploying machine learning models and intelligent systems into production environments. It is crucial to ensure that these systems are robust, scalable, and performant. - Software Design & Architecture: You will apply strong software engineering principles to design and build clean, modular, testable, and maintainable ML pipelines, APIs, and services. Your contribution to the architectural decisions for the ML platform and applications will be significant. - Data Engineering for ML: Designing and implementing data pipelines for feature engineering, data transformation, and data versioning to support ML model training and inference will be part of your responsibilities. - MLOps & Productionization: You will 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. - Performance & Scalability: Identifying and resolving performance bottlenecks in ML systems, and ensuring the scalability and reliability of deployed models under varying load conditions will be essential. - Documentation: Creating clear and comprehensive documentation for ML models, pipelines, and services is a key part of this role. - Good to have Machine Learning Expertise: Having a solid theoretical and practical understanding of various machine learning algorithms, proficiency with ML frameworks like PyTorch, Scikit-learn, and experience with feature engineering, model evaluation metrics, and hyperparameter tuning will be advantageous.