Software Engineer (Risk Control System)

About RedotPay

RedotPay is a global crypto payment fintech company headquartered in Hong Kong, focused on integrating blockchain solutions with traditional financial systems. Our user-friendly platform empowers millions worldwide to spend and send crypto assets seamlessly, promoting faster, more accessible, and inclusive financial services. We are dedicated to advancing financial inclusion for the unbanked and supporting crypto enthusiasts by driving the adoption of secure, flexible crypto-powered solutions. Join our dynamic team to shape the future of finance and contribute to meaningful global impact.

Position Overview

Lead the design and development of a high-performance risk control platform for real-time fraud prevention, credit risk decisioning, and compliance in a fast-paced fintech environment.

Key Responsibilities

• System Architecture: Architect the risk control system from the ground up — including technology selection, real-time feature engines, dynamic rule management, risk profiling, and multi-strategy decision workflows. Design for high concurrency and low latency.


• Data & Model Engineering: Build dedicated risk data warehouse and feature pipelines (real-time/offline). Integrate ML models (anti-fraud, credit scoring) with rule strategies; develop automated model deployment and A/B testing frameworks.


• Implementation & Reliability: Lead core module development to achieve 99.99% availability. Implement monitoring, alerting, and disaster recovery solutions under high-traffic conditions.


• Integration & Enablement: Drive seamless integration with payment, transaction, and user systems via standardized APIs. Document best practices and mentor the team on risk control technologies.

Requirements

• Bachelor or above in Computer Science, Engineering, or related IT field.


• Proven experience leading technical selection and architecture for risk control systems


• Hands-on experience designing hybrid architectures supporting both real-time anti-fraud detection and offline credit risk assessment at scale.


• Expert in Java and microservices. Strong cloud-native skills with Docker, Kubernetes, and Service Mesh.


• Big Data & Real-Time: Proficient in Flink/Spark Streaming for real-time feature engines, Kafka/Pulsar messaging, and large-scale storage.


• Risk Control Tech: Experience with rule engines (Drools/Aviator), decision flow engines (Camunda), feature engineering pipelines, real-time feature stores, and model serving (TensorFlow Serving/MLflow) with A/B testing.


• Understanding of common fraud attack vectors in payments/fintech (credential stuffing, cash-out, abuse patterns) and ability to design targeted countermeasures.