Develop, deploy, and optimize machine learning models for fraud detection, risk scoring, anomaly detection, and related security applications.
Build scalable AI and ML pipelines supporting model training, validation, deployment, monitoring, and retraining.
Design and implement Generative AI and Agentic AI solutions leveraging Large Language Models (LLMs), retrieval-augmented generation (RAG), reasoning and planning workflows, Chain-of-Thought-style prompting techniques, tool/function calling, LLM-based modular systems, and multi-agent orchestration.
Partner with AI Engineers and Data Scientists to productionize advanced models and translate research into scalable business solutions.
Work with large-scale transactional datasets using distributed computing technologies such as Spark and Databricks.
Implement MLOps best practices, including CI/CD, model governance, observability, experimentation frameworks, and performance monitoring.
Contribute to the design of reusable AI platforms, libraries, and engineering frameworks that accelerate AI development across teams.
Evaluate emerging AI technologies and identify opportunities to apply them to fraud prevention and security challenges.
Contribute to Mastercard's innovation efforts by publishing top-tier research papers, filing patents, and translating applied AI research into scalable fraud and security solutions.
Ensure solutions meet Mastercard standards for scalability, reliability, explainability, security, and responsible AI.