Job
Description
As a Machine Learning Engineer at our company, you will play a crucial role in developing and deploying machine learning models and intelligent systems. Your responsibilities will include: - **Coding:** Write clean, efficient, and well-documented Python code following OOP principles. You should have experience with Python and related libraries such as TensorFlow, PyTorch, and Scikit-Learn. Your tasks will involve handling the entire ML pipeline, from data ingestion and preprocessing to model training, evaluation, and deployment. - **End-to-End ML Application Development:** Design, develop, and deploy machine learning models into production environments, ensuring they are robust, scalable, and performant. - **Software Design & Architecture:** Apply strong software engineering principles to design clean, modular, and maintainable ML pipelines, APIs, and services. You will also contribute significantly to 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 best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring, and alerting systems for production ML models. - **Performance & Scalability:** Identify and resolve performance bottlenecks in ML systems, ensuring scalability and reliability under varying load conditions. - **Documentation:** Create clear and comprehensive documentation for ML models, pipelines, and services. Qualifications required for this role: - **Education:** Master's degree in computer science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field. - **Experience:** 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role. - **Programming Skills:** Expert-level proficiency in Python, including writing production-grade, clean, and efficient code. Experience with other languages like Java, Go, or C++ is a plus. - **Software Engineering Fundamentals:** Deep understanding of software design patterns, data structures, algorithms, object-oriented programming, and distributed systems. Additionally, it's good to have expertise in various machine learning algorithms, ML frameworks like PyTorch and Scikit-learn, feature engineering, model evaluation metrics, hyperparameter tuning, data handling with SQL and NoSQL databases, and problem-solving skills. In summary, as a Machine Learning Engineer, you will be responsible for developing and deploying machine learning models and systems, applying strong software engineering principles, and ensuring the scalability and reliability of ML systems. Your expertise in Python coding, software engineering fundamentals, and problem-solving skills will be essential for success in this role. As a Machine Learning Engineer at our company, you will play a crucial role in developing and deploying machine learning models and intelligent systems. Your responsibilities will include: - **Coding:** Write clean, efficient, and well-documented Python code following OOP principles. You should have experience with Python and related libraries such as TensorFlow, PyTorch, and Scikit-Learn. Your tasks will involve handling the entire ML pipeline, from data ingestion and preprocessing to model training, evaluation, and deployment. - **End-to-End ML Application Development:** Design, develop, and deploy machine learning models into production environments, ensuring they are robust, scalable, and performant. - **Software Design & Architecture:** Apply strong software engineering principles to design clean, modular, and maintainable ML pipelines, APIs, and services. You will also contribute significantly to 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 best practices for MLOps, including CI/CD for ML, automated testing, model versioning, monitoring, and alerting systems for production ML models. - **Performance & Scalability:** Identify and resolve performance bottlenecks in ML systems, ensuring scalability and reliability under varying load conditions. - **Documentation:** Create clear and comprehensive documentation for ML models, pipelines, and services. Qualifications required for this role: - **Education:** Master's degree in computer science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field. - **Experience:** 5+ years of professional experience in Machine Learning Engineering, Software Engineering with a strong ML focus, or a similar role. - **Programming Skills:** Expert-level proficiency in Python, including wr
