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 a Data Engineer with expertise in Machine Learning, you will apply Machine Learning concepts and techniques to address business challenges. You will leverage your skills to drive informed decision-making in the organization. Your primary responsibilities will include:
- Develop Machine Learning Solutions: Apply Machine Learning concepts and techniques to address business challenges, interpreting statistical data and identifying relevant features to inform solution development.
- Evaluate Algorithm Performance: Choose appropriate algorithms and evaluate their performance using relevant metrics, ensuring that solutions meet business needs and drive informed decision-making.
- Communicate Results: Clearly communicate the results of Machine Learning initiatives to stakeholders, providing actionable insights that inform business decisions.
- Implement Machine Learning Techniques: Collaborate with stakeholders to implement Machine Learning techniques that drive business value, selecting and applying relevant methodologies to achieve desired outcomes.
Preferred Education
Bachelor's Degree
Required Technical And Professional Expertise
Cloud & ML Platforms
- Hands-on experience with Azure ML and/or AWS SageMaker for model training, deployment, and management
- Familiarity with cloud-native services (Azure, AWS) for data storage, compute, and networking
- Container Orchestration & Infrastructure
- Proficient in Kubernetes for deploying and scaling ML workloads
- Experience with Kubeflow for orchestrating ML pipelines on Kubernetes is a plus
ML Lifecycle & Experiment Tracking
- Hands-on experience with ML flow for experiment tracking, model registry, and deployment
- Solid understanding of the end-to-end ML lifecycle — from data ingestion to model monitoring
Core MLOps Practices
- Building and maintaining CI/CD pipelines for ML workflows
- Model versioning, monitoring, and retraining strategies
Programming & Scripting
- Proficient in Python (primary language for ML/MLOps tooling
Familiarity with SQL and data pipeline tools
Infrastructure as Code (IaC) — Terraform or Helm charts
Soft Skills & Collaboration
- Ability to bridge the gap between Data Science and Engineering teams
- Strong communication and documentation skills
- Experience working in Agile/DevOps environments
Nice to Have
- Experience with Kubeflow Pipelines
- Knowledge of feature stores
- Familiarity with data versioning tools (DVC, Delta Lake)
- Experience with model governance and compliance
Preferred Technical And Professional Experience
- Advanced Algorithm Development: Experience working with complex algorithms, including evaluating their performance using relevant metrics and fine-tuning for optimal results.
- Data Visualization Techniques: Exposure to data visualization tools and techniques, enabling effective communication of Machine Learning results to stakeholders.
- Specialized Machine Learning Tools: Familiarity with specialized Machine Learning tools and technologies, such as those used for natural language processing or computer vision.
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