ML Engineer-Advanced Analytics

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

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.

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.

Preferred Technical And Professional Experience

• 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.