Senior Machine Learning Engineer - Fraud Detection

<p><strong>SENIOR MACHINE LEARNING ENGINEER</strong><br><br>We are recruiting Senior Machine Learning Engineers to work on the development of a next-generation fraud detection platform for a major Payment Service Provider (PSP).<br>The role combines production-grade machine learning engineering, advanced data analysis/statistics, and customer-facing technical collaboration. You will work closely with the client’s data, engineering, risk, and compliance teams to design, implement, deploy, and continuously improve real-time ML models operating in a highly regulated financial environment.<br>We approach these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us. This is a hands-on, forward-deployed role requiring both deep technical expertise and strong communication skills in English.<br><br><strong>Core Responsibilities</strong></p><ul><li><p>Design, train, evaluate, and deploy ML models for transaction-level fraud detection (primarily tabular data).</p></li><li><p>Analyze large-scale transaction datasets to identify patterns, leakage, bias, and data quality issues.</p></li><li><p>Build and maintain production ML services (real-time and batch).</p></li><li><p>Implement robust ML pipelines, model monitoring, and experiment frameworks.</p></li><li><p>Collaborate directly with client engineers, data scientists, and risk teams.</p></li><li><p>Translate complex technical concepts and results into clear, actionable insights for technical and non-technical stakeholders.</p></li><li><p>Operate within strict requirements for reliability, explainability, traceability, and compliance.<br></p></li></ul><p><strong>Background and skills:</strong></p><ul><li><p>Production-grade Python and solid ML fundamentals (XGBoost/LightGBM, Scikit-learn, feature engineering, imbalanced datasets)</p></li><li><p>Experience building and shipping ML-powered APIs (FastAPI/Flask), Docker, CI/CD, and distributed data processing (PySpark/SQL)</p></li><li><p>Strong stats foundation: experimental design, bias/leakage detection, time-dependent validation</p></li><li><p>Hands-on MLOps experience — feature stores, Airflow/Kubeflow, model monitoring, real-time inference, A/B testing</p></li><li><p>MSc or Ph.D. in a quantitative field</p></li><li><p>Excellent understanding of a broad set of ML algorithms and frameworks</p></li><li><p>A passion for lean, clean, and maintainable code</p></li><li><p>The desire to grow and to share insights with others<br></p></li></ul><div><p><strong>Domain experience:</strong><span> </span>Fraud detection, payments, fintech, or credit risk. You've worked with cost-sensitive decisions, highly imbalanced data, and models that directly impact business risk.<br><br><strong>How you work:</strong><span> </span>You communicate clearly with engineers, product, and compliance stakeholders alike. You write good documentation and can hold your own in architecture discussions.</p></div><p></p><p><br><strong>About Team Modulai</strong><br><br>At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build our own ML products.</p><p>Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but never do it because we always work in teams. If there is data, we will do ML on it!</p>