Machine Learning Engineer with strong expertise in designing, deploying, and optimizing end-to-end ML systems, particularly within data-intensive and reliability-critical domains. Skilled in Python, feature engineering, and modern ML frameworks, I bring a deep analytical approach to understanding transaction patterns and improving model performance in production. Experienced with cloud platforms and real-time deployment pipelines, I excel at building scalable, secure, and well-documented solutions that integrate seamlessly with engineering and product workflows. With a strong foundation in algorithmic thinking, problem-solving, and collaboration, I am driven to create ML models that directly enhance payment success rates, customer experience, and overall system resilience. I have led research and industry projects across academia and fintech, delivering 10+ applied ML initiatives, securing funding, and mentoring teams. In my current role at The University of Melbourne, I design reinforcement learning systems, deploy scalable simulations with ML Ops, and build real-time dashboards to monitor fraud risk, reducing manual workload and deployment times. My work emphasizes rigorous data quality, cross-functional collaboration, and translating complex results into actionable product and risk strategies.…Machine Learning Engineer with strong expertise in designing, deploying, and optimizing end-to-end ML systems, particularly within data-intensive and reliability-critical domains. Skilled in Python, feature engineering, and modern ML frameworks, I bring a deep analytical approach to understanding transaction patterns and improving model performance in production. Experienced with cloud platforms and real-time deployment pipelines, I excel at building scalable, secure, and well-documented solutions that integrate seamlessly with engineering and product workflows. With a strong foundation in algorithmic thinking, problem-solving, and collaboration, I am driven to create ML models that directly enhance payment success rates, customer experience, and overall system resilience. I have led research and industry projects across academia and fintech, delivering 10+ applied ML initiatives, securing funding, and mentoring teams. In my current role at The University of Melbourne, I design reinforcement learning systems, deploy scalable simulations with ML Ops, and build real-time dashboards to monitor fraud risk, reducing manual workload and deployment times. My work emphasizes rigorous data quality, cross-functional collaboration, and translating complex results into actionable product and risk strategies.WWWW…