Senior Machine Learning Engineer – AI/ML Solutions

About the role

Mastercard’s Business & Market Insights (B&MI) group is seeking a Senior Engineer to lead the Operational Intelligence Program. The role focuses on designing, building, and delivering secure, scalable AI/ML solutions that drive data‑driven insights for financial and operational use cases.

Key responsibilities

• Lead design and development of AI and analytics solutions covering classical machine learning, time‑series forecasting, statistical modeling, deep learning, and emerging agent‑based architectures.
• Develop predictive and prescriptive models using supervised, unsupervised, and probabilistic approaches such as regression, tree‑based models, clustering, anomaly detection, Bayesian inference, and ensemble methods.
• Build and optimize forecasting frameworks with ARIMA/SARIMA, ETS, Prophet, VAR, state‑space models, LSTM/GRU, and hybrid ML pipelines.
• Integrate Generative AI and multi‑agent systems (LangGraph, CrewAI, AutoGen) with traditional ML to enable reasoning‑driven automation and intelligent decision support.
• Perform exploratory data analysis, feature engineering, hypothesis testing, experimental design, root‑cause analysis, and uncertainty quantification.
• Create reusable model components, evaluation workflows, hyperparameter tuning, drift detection, and benchmarking across classical ML and GenAI.
• Ensure model governance, explainability, and responsible AI practices.

Required profile

• Senior‑level technologist with strong experience in AI/ML solution delivery.
• Proven ability to manage stakeholders and drive engineering best practices.
• Commitment to continuous learning and fostering technical excellence within a team.

Required skills

• Classical machine learning
• Time‑series forecasting (ARIMA, SARIMA, ETS, Prophet, VAR, state‑space models, LSTM, GRU)
• Statistical modeling and deep learning
• Agent‑based architectures, Generative AI, LangGraph, CrewAI, AutoGen
• Supervised, unsupervised, and probabilistic modeling
• Regression, tree‑based models, clustering, anomaly detection, Bayesian inference, ensemble methods
• Feature engineering, hypothesis testing, experimental design, root‑cause analysis, uncertainty quantification
• Model governance, explainable AI, responsible AI practices