About this role
Your Role and Responsibilities:As an ML Engineer with Advanced Analytics skills, you have a strong understanding of machine learning techniques and their applications. You possess the ability to apply various ML algorithms to design, develop, and deploy production-ready ML software, distributed systems, and ML components.Your Primary Responsibilities Will Include:
- Design ML Solutions: Design production-ready ML software, distributed systems, and ML components that are efficient, scalable, and maintainable.
- Develop ML Algorithms: Apply various ML algorithms, including regression, classification, clustering, and recommender systems, using popular libraries such as scikit-learn, TensorFlow, or PyTorch.
- Deploy ML Systems: Deploy designed ML solutions, ensuring they meet production standards and are integrated with existing systems.
- Optimize ML Performance: Ensure ML solutions are efficient, scalable, and maintainable, and optimize their performance as needed.
- Maintain ML Components: Maintain and update existing ML components to ensure they remain efficient and effective.
Required Education: Bachelor's Degree Preferred Education: Master's Degree Required Technical and Professional Expertise:
- Machine Learning Techniques: Experience with machine learning techniques and their applications, including regression, classification, clustering, and recommender systems.
- ML Algorithm Development: Experience in applying various ML algorithms using popular libraries such as scikit-learn, TensorFlow, or PyTorch to design and develop production-ready ML software and components.
- Distributed Systems Design: Experience designing and deploying distributed systems and ML components that are efficient, scalable, and maintainable.
- Production-Ready Software Development: Experience developing and deploying production-ready ML software that meets production standards and integrates with existing systems.
- Performance Optimization: Experience optimizing the performance of ML solutions to ensure they remain efficient and effective.
- Familiarity with ML Libraries: Experience with popular libraries such as scikit-learn, TensorFlow, or PyTorch is beneficial for designing and developing production-ready ML software and components.
- Knowledge of Distributed Systems: Understanding of distributed systems design principles is advantageous for deploying efficient, scalable, and maintainable ML components.
- Exposure to Performance Optimization: Familiarity with optimizing the performance of ML solutions is desirable to ensure they remain efficient and effective.
Skills
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