Develop backend services for real-time fraud detection, transaction monitoring, and risk analysis.

Build and maintain scalable microservices to support risk assessment models and rule-based detection systems.

Integrate machine learning models and risk scoring engines into backend systems.

Participate in AI-assisted development initiatives, including automated code generation, intelligent alerting, and integration of LLM-powered risk operations tools.

Proficiency in Java (Spring Boot), basic frameworks, such as multithreading and networking.

Understanding of distributed systems, and Spring microservices, dubbo.

Experience with SQL & NoSQL databases (MySQL, Redis, Hive, Starrocks).

Familiarity with message queues & streaming (Kafka, RabbitMQ).

Enthusiasm for and hands-on experience with AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor, or similar coding assistants); ability to leverage AI to accelerate code generation, code review, and unit test coverage.

Basic understanding of how LLM/Agent systems work and how they integrate into backend services; interest in building or iterating AI-powered risk automation features.

Familiarity with prompt engineering concepts and the ability to work alongside AI models in daily development workflows.

Strong analytical thinking, excellent teamwork skills, and a keen ability to learn and adapt quickly.