ML Engineer-Advanced Analytics

Introduction

Build a career in IBM Consulting grounded in enduring client partnerships and global teamwork. You’ll collaborate with leading organizations across industries, supporting their hybrid cloud and AI transformation. Backed by strategic partners, advanced IBM technology, and Red Hat, you’ll help drive meaningful change and speed up client outcomes. At IBM Consulting, curiosity is the engine for success. You’ll be encouraged to question conventions, pursue new ideas, and develop solutions that produce tangible results. Our growth- and empathy-centered culture supports your long-term career development while recognizing the value of your individual skills and experiences.

Your Role And Responsibilities

As an ML Engineer with Advanced Analytics skills, you will leverage your strong knowledge of machine learning techniques and their real-world applications to design, develop, and deploy production-ready machine learning software. You will use widely adopted libraries such as scikit-learn, TensorFlow, or PyTorch to support ML solutions that are efficient, scalable, and maintainable.

Your Primary Responsibilities Will Include

• Design and Develop ML Software: Use a range of ML algorithms, including regression, classification, clustering, and recommender systems, to build production-ready ML software.
• Deploy and Maintain ML Solutions: Keep ML solutions efficient, scalable, and maintainable, and deploy them to align with business requirements.
• Apply ML Techniques: Apply machine learning techniques to address complex challenges and enhance system performance.
• Develop Distributed Systems: Create and build distributed systems and ML components that enable large-scale ML applications.
• Implement ML Algorithms: Implement ML algorithms with popular libraries such as scikit-learn, TensorFlow, or PyTorch to deliver business impact.
  
  

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Machine Learning Techniques: Exposure to applying a variety of ML algorithms, including regression, classification, clustering, and recommender systems, to solve complex problems and improve system performance.
• ML Software Development: Experience using 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 deploying and maintaining ML solutions to ensure they remain 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 using 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 that support large-scale ML applications.
• ML Algorithm Implementation: Exposure to implementing ML algorithms using popular libraries to drive business outcomes.