Machine Learning Engineer - Risk Data Experienced (Individual Contributor) Singapore Data Science

Monee is a leading digital payments and financial services provider in Southeast Asia, with a growing presence in Latin America. Its mission is to better the lives of individuals and businesses in the region with financial services through technology. Monee delivers a comprehensive range of digital financial products and services, including mobile wallets, payment processing, credit, banking, and insurtech. By leveraging technology and strategic partnerships, Monee aims to drive greater digital financial inclusion, convenience, and accessibility for individuals and small businesses across Southeast Asia and Latin America. Monee is part of Sea Limited, a global technology company. About the team: We are looking for a Machine Learning Engineer to join our team, building data and machine learning infrastructure for Credit Risk systems. In this role, you will work closely with Data Scientists, Risk Policy, and Engineering teams to help productionize machine learning models, maintain large-scale data pipelines, and support real-time decision systems. You will also have the opportunity to explore how AI technologies can improve model monitoring, data quality, and developer productivity across the risk team. This role is ideal for engineers who enjoy working at the intersection of data engineering, machine learning systems, and platform infrastructure, and who want to build reliable systems supporting risk decisioning at scale. Job Description: • Support the deployment of machine learning models into real-time and batch risk decision systems. • Build and maintain infrastructure for model serving, distributed inference, and production ML workflows. • Develop and maintain large-scale data pipelines supporting model training, scoring, and inference. • Work with Data Scientists to ensure feature consistency between offline model development and online production environments. • Improve ETL / ELT workflows for large-scale risk data processing. • Build and enhance monitoring systems for data quality, feature drift, model performance, and pipeline health. • Investigate pipeline failures, data inconsistencies, and production issues across model and data systems. • Improve observability, reliability, and scalability across data pipelines and model services. • Explore the use of AI technologies to improve engineering workflows, including pipeline diagnostics, data quality validation, model monitoring analysis, and developer productivity tools. • Collaborate closely with Data Science, Risk Policy, Data Engineering, and Software Engineering teams to support ML systems powering risk decisioning across multiple markets. Requirements: • Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Data Science, Artificial Intelligence, Machine Learning, Computer Engineering, Statistics, Applied Mathematics, or a related field. • Minimum 3 years of experience in Data Engineering, Machine Learning Engineering, ML Platform Engineering, or a related technical role. • Hands-on experience building data pipelines, ML systems, or production data infrastructure. • Strong programming skills in Python and experience with Spark. • Experience with large-scale data processing frameworks such as Spark, Flink, or similar technologies. • Experience with workflow orchestration tools such as Airflow or similar. • Familiarity with machine learning workflows, including model deployment, inference, monitoring, and production support. • Good understanding of distributed systems, data infrastructure, and production system reliability. • Comfortable working with large-scale datasets and troubleshooting production issues. • Good communication skills and ability to work cross-functionally with Data Science, Risk, and Engineering teams. #J-18808-Ljbffr