Overview

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide.

Responsibilities

• 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.
• 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.