ML Engineer-Advanced Analytics

Overview

A career with IBM Consulting emphasizes the importance of fostering long-term relationships with clients and collaborating on a global scale. You will have the opportunity to partner with top-tier companies across various industries, assisting them in navigating their hybrid cloud and AI transformations. Supported by our strategic alliances, cutting-edge IBM technology, and Red Hat, you will be equipped to implement significant changes and enhance client outcomes. At IBM Consulting, a spirit of curiosity drives our achievements. You will be encouraged to question the status quo, explore innovative concepts, and devise creative solutions that yield tangible results. Our culture prioritizes growth and empathy, focusing on your career development while appreciating your distinctive skills and experiences.

Your Role and Responsibilities

As a Machine Learning Engineer specializing in Advanced Analytics, you possess a comprehensive understanding of machine learning methodologies and their practical applications. You are adept at leveraging various ML algorithms to design, develop, and implement production-ready ML software, distributed systems, and ML components.

Your Key Responsibilities Will Involve

• ML Solution Design: Develop production-ready ML software, distributed systems, and ML components that prioritize efficiency, scalability, and maintainability.
• Algorithm Development: Utilize diverse ML algorithms, such as regression, classification, clustering, and recommender systems, employing popular libraries like scikit-learn, TensorFlow, or PyTorch.
• System Deployment: Implement ML solutions, ensuring they adhere to production standards and seamlessly integrate with existing systems.
• Performance Optimization: Enhance the efficiency and scalability of ML solutions, optimizing performance as required.
• Component Maintenance: Regularly maintain and update existing ML components to ensure ongoing efficiency and effectiveness.
  
  

Preferred Educational Background

Master's Degree

Essential Technical and Professional Expertise

• Expertise in Machine Learning Techniques: Proficient in various machine learning techniques and their uses, including regression, classification, clustering, and recommender systems.
• ML Algorithm Application: Experienced in applying different ML algorithms using mainstream libraries such as scikit-learn, TensorFlow, or PyTorch to create and develop production-ready ML software and components.
• Distributed Systems Knowledge: Familiar with designing and deploying distributed systems and ML components that are efficient, scalable, and maintainable.
• Production Software Development: Proven experience in developing and deploying production-ready ML software that meets established production standards and integrates with existing frameworks.
• Performance Enhancement: Skilled in optimizing the performance of ML solutions to ensure they are effective and efficient.
  
  

Desirable Technical and Professional Experience

• Familiarity with ML Libraries: Experience with key libraries such as scikit-learn, TensorFlow, or PyTorch is advantageous for crafting and developing production-ready ML software and components.
• Understanding of Distributed Systems: Knowledge of distributed systems design principles is a plus for deploying efficient, scalable, and maintainable ML components.
• Experience in Performance Optimization: Background in optimizing ML solution performance is beneficial to maintain their efficiency and effectiveness.