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 will apply your strong understanding of machine learning techniques and their applications to design, develop, and deploy production-ready ML software. You will utilize popular libraries such as scikit-learn, TensorFlow, or PyTorch to ensure that ML solutions are efficient, scalable, and maintainable. Your primary responsibilities will include:

• Design and Develop ML Software: Apply various ML algorithms, including regression, classification, clustering, and recommender systems, to create production-ready ML software.
• Deploy and Maintain ML Solutions: Ensure that ML solutions are efficient, scalable, and maintainable, and deploy them to meet business needs.
• Apply ML Techniques: Utilize machine learning techniques to solve complex problems and improve system performance.
• Develop Distributed Systems: Design and develop distributed systems and ML components to support large-scale ML applications.
• Implement ML Algorithms: Implement ML algorithms using popular libraries such as scikit-learn, TensorFlow, or PyTorch to drive business outcomes.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Machine Learning Techniques: Exposure to applying various ML algorithms, including regression, classification, clustering, and recommender systems, to solve complex problems and improve system performance.
• ML Software Development: Experience working with popular libraries such as scikit-learn, TensorFlow, or PyTorch to design, develop, and deploy production-ready ML software.
• Distributed Systems: Exposure to designing and developing distributed systems and ML components to support large-scale ML applications.
• ML Solution Deployment: Experience working with deploying and maintaining ML solutions to ensure they are efficient, scalable, and maintainable.
• Algorithm Implementation: Exposure to implementing ML algorithms using popular libraries to drive business outcomes.

Preferred Technical And Professional Experience

• Popular Library Proficiency: Exposure to utilizing popular libraries such as scikit-learn, TensorFlow, or PyTorch for designing, developing, and deploying production-ready ML software.
• Distributed System Knowledge: Exposure to designing and developing distributed systems and ML components to support large-scale ML applications.
• ML Algorithm Implementation: Exposure to implementing ML algorithms using popular libraries to drive business outcomes.