Senior Machine Learning Engineer

s to solve complex problems. The ideal candidate is a hands-on technical expert with deep knowledge of machine learning, software engineering, and data infrastructure.

Role:
Responsible for developing machine learning-driven analytical solutions and identifying opportunities to support business and client needs in a scalable and automated manner, facilitating informed recommendations and decisions. Activities include designing and deploying ML models, building end-to-end pipelines, conducting performance analyses, ad hoc reporting, and developing ML-powered data visualizations.

In this position, you will:
Lead complex technical initiatives to build and deploy ML systems that solve critical business questions and automate decision-making processes
Translate business and stakeholder requirements into scalable machine learning solutions and collaborate with engineering and data teams to implement them
Identify rich data sources and oversee the integration, cleaning, and transformation of datasets to ensure consistency and readiness for ML applications
Deliver high-quality ML solutions and tools within agreed-upon timelines and budget parameters and conduct post-implementation reviews
Develop sophisticated ML models and engineering solutions (e.g., recommendation systems, anomaly detection engines, predictive maintenance tools) using supervised, unsupervised, and reinforcement learning techniques
Apply best practices in software engineering, including version control, testing, and continuous integration, to ensure reliability and maintainability of ML systems
Optimize model performance and scalability through hyperparameter tuning, feature selection, and efficient deployment strategies

All about you:
3 - 5 years proven experience designing, building, and deploying machine learning systems in production environments
Strong proficiency in Python and ML frameworks such as Scikit-learn, TensorFlow, PyTorch
Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes)
Solid understanding of ML algorithms, model evaluation, feature engineering, and data preprocessing
Experience with complex neural network architectures and transformer-based models (e.g., BERT, GPT, ViTs) is strongly preferred
Familiarity with MLOps practices including CI/CD, model monitoring, and automated retraining
Strong problem-solving skills and ability to work independently on technically challenging projects
Excellent communication skills and ability to collaborate effectively with cross-functional teams

#AI

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard's security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.