Backend Engineer (Risk & Fraud)
Lalamove is disrupting the logistics industry by connecting customers and drivers directly through our technology.We offer customers a lightning-fast and convenient way to book delivery and moving services whether they are at their home, at work, or on the go.People talk about O2O, we live it! Currently, Lalamove is a leadingglobal on-demand delivery platform with millions of delivery partners serving millions of orders every day. With 1600+ employees spread across SEA, and LATAM, in 5 years' time’ our company has reached unicorn status in 2018, is well funded by prominent VCs, and has kept growing at tremendous speed since.Our strength lies in our internal values, namelyPassionin serving local communities, empowering SMEs and our driver-partners,ExecutionandGritbecause that is how we differentiate ourselves by never giving up and striving for excellence, andHumility- awareness in ourselves to learn from others and never stop improving.Due to the rapid growth in the volume of our business coverage, Lalamove is now seeking a proactive Risk Engineer to develop robust risk management systems protecting our logistics platform from fraud, operational risks, and abuse. You will design data-driven solutions integrating real-time data pipelines, and advanced APIs. Your work will ensure platform integrity for millions of global delivery transactions. What you'll do
Design and implement data pipelines using Kafka for real-time data streaming and processing.Develop risk detection systems using Java, Python, Groovy, C++, and R, with a focus on scalability and high availability. Develop backend microservices focused on fraud risk using Java and Spring Boot Build scalable distributed systems on cloud platforms like AWS Build and maintain APIs integrating risk services across Lalamove’s platform components. Design and implement real-time fraud monitoring tools with configurable rules and data stream ingestionIntegrate machine learning models to detect anomalies, assign risk scores, and adapt to emerging fraud patterns Collaborate with data scientists to leverage advanced analytics for fraud prevention Create data simulation models to predict and mitigate logistics risks (e.g., fraud, operational failures). Ensure data quality and integrity through validation rules, monitoring, and governance frameworks. Analyze key risk indicators (KRIs) to quantify exposure and drive mitigation strategies. Lead technical initiatives, optimize system performance, and ensure 24/7 reliabilityWrite clean, tested code and participate in CI/CD processes What you'll need 5+ years of software development experience with strong Java expertise and JVM tuning Familiarity with Spring Framework, Hibernate, and databases like MySQL or Oracle Knowledge of caching systems (Redis, Memcached) and middleware (Kafka, ELK) Experience with REST APIs, message queues, and distributed architectures Strong understanding of data modeling, simulation, and pipeline optimization. Familiarity with machine learning applications in fraud/risk contexts. Solid understanding of distributed systems and concurrency Strong problem-solving skills, teamwork, and communication (English required; Mandarin a plus) Experience or interest in applying machine learning and data science techniques for fraud detection and prevention Additional Information To all candidates- Lalamove respects your privacy and is committed to protecting your personal data.This Notice will inform you how we will use your personal data, explain your privacy rights and the protection you have by the law when you apply to join us. Please take time to read and understand this Notice. Candidate Privacy Notice: https://www.lalamove.com/en-hk/candidate-privacy-notice
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Design and implement data pipelines using Kafka for real-time data streaming and processing.Develop risk detection systems using Java, Python, Groovy, C++, and R, with a focus on scalability and high availability. Develop backend microservices focused on fraud risk using Java and Spring Boot Build scalable distributed systems on cloud platforms like AWS Build and maintain APIs integrating risk services across Lalamove’s platform components. Design and implement real-time fraud monitoring tools with configurable rules and data stream ingestionIntegrate machine learning models to detect anomalies, assign risk scores, and adapt to emerging fraud patterns Collaborate with data scientists to leverage advanced analytics for fraud prevention Create data simulation models to predict and mitigate logistics risks (e.g., fraud, operational failures). Ensure data quality and integrity through validation rules, monitoring, and governance frameworks. Analyze key risk indicators (KRIs) to quantify exposure and drive mitigation strategies. Lead technical initiatives, optimize system performance, and ensure 24/7 reliabilityWrite clean, tested code and participate in CI/CD processes What you'll need 5+ years of software development experience with strong Java expertise and JVM tuning Familiarity with Spring Framework, Hibernate, and databases like MySQL or Oracle Knowledge of caching systems (Redis, Memcached) and middleware (Kafka, ELK) Experience with REST APIs, message queues, and distributed architectures Strong understanding of data modeling, simulation, and pipeline optimization. Familiarity with machine learning applications in fraud/risk contexts. Solid understanding of distributed systems and concurrency Strong problem-solving skills, teamwork, and communication (English required; Mandarin a plus) Experience or interest in applying machine learning and data science techniques for fraud detection and prevention Additional Information To all candidates- Lalamove respects your privacy and is committed to protecting your personal data.This Notice will inform you how we will use your personal data, explain your privacy rights and the protection you have by the law when you apply to join us. Please take time to read and understand this Notice. Candidate Privacy Notice: https://www.lalamove.com/en-hk/candidate-privacy-notice
Apply Now