Backend Software Engineer (Java, Fraud Detection & Risk Decisioning)

Fraud Prevention EngineerRequired Education: Bachelor's degree in computer science or a related field. Preferred Education: Master's degree in computer science or a related field.Required Skills: 2+ years of professional software engineering experience building and contributing to backend services (SWE2 level); proficiency in a server-side language, especially Java (our primary stack); Python or Node.js also valuable (3-5+ Years); solid experience with RESTful API design and building/maintaining API integrations (3-5+ Years); experience with database technologies (relational and/or NoSQL); experience with a server framework such as Spring MVC (preferred), Express, or Django; hands-on experience contributing to a production service — production-quality code and tests, deployment, and on-call; proficiency with AI coding tools (Claude Code, Cursor, Codex, etc.) and the judgment to use them effectively while validating their output; solid grasp of Agile development, object-oriented design, and engineering best practices; strong communication and collaboration skills across engineering, policy, and analytics partners; eagerness to learn and adapt in a dynamic, fast-moving environment.Preferred Skills: Familiarity with business rules engines (e.g., Drools) — authoring and debugging rule sets, ideally with AI assistance; experience turning fraud/risk policy into rule logic, or working with risk decisioning, case-disposition, or trust & safety systems (2+ years); experience contributing to backend services at scale (design, reliability, performance); exposure to formal release and change-management processes and to observability/monitoring tooling; interest in fraud prevention, trust & safety, or risk systems.Duties: Author, maintain, and optimize fraud rules and policies across our real-time fraud decisioning engines to protect customers from account takeover (ATO) and other fraud — this is the core of the role. Contribute significantly to the backend services in the fraud-prevention stack: design and implement features, API changes, and integrations; make production code changes; and help improve their reliability, performance, and quality — not just their rules. Use AI coding tools as a core part of your workflow (Claude Code, Cursor, Codex) to accelerate both rule authoring and service development — generating, refactoring, and validating rule sets, tests, and service code, always with human review and testing. Translate policy and analytics requirements into precise, testable rule logic — tuning thresholds, fraud signals, and link-analysis weights to raise fraud-catch precision while reducing friction for legitimate customers. Test changes end-to-end before release using pre-release testing and artefact-deployment tooling, and manage safe production rollouts — including silent/shadow-mode validation and change-request approvals. Contribute to engineering quality across the stack: design docs, code reviews, automated tests, CI/CD, and observability. Support monitoring, on-call, and rapid issue resolution in production, using observability dashboards to track service health, rule performance, latency, and match rates. Collaborate closely with Policy, Analytics, Data Science, and Product partners to respond to emerging fraud attacks and continuously improve detection coverage.**Only those lawfully authorized to work in the designated country associated with the position will be considered.****Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client's business needs and requirements.**