Software Engineer

About Rasēd

Rasēd is a pioneering fraud detection and prevention platform engineered for financial institutions, fintechs, and payment providers. By leveraging the power of artificial intelligence, device intelligence, and behavioral biometrics, Rasēd delivers real-time fraud detection and prevention while maintaining an optimal user experience. The platform integrates advanced analytics for post-incident investigations, compliance adherence, and automated case management, empowering organizations to achieve faster, smarter, and more secure fraud management solutions.

Location & Employment

This full-time position is based in Riyadh, Saudi Arabia, and requires onsite presence to collaborate effectively within the team.

Role Overview

The Software Engineer will play a pivotal role in designing, developing, and maintaining scalable services and platforms that underpin Rasēd’s fraud detection, transaction monitoring, and risk decisioning capabilities within a highly regulated financial environment. This role demands a blend of technical expertise, collaborative problem-solving, and a commitment to delivering high-performance solutions that align with industry standards and regulatory requirements.

Key Responsibilities

• Backend Development & API Engineering: Design, develop, and maintain robust backend services and APIs that drive Rasēd’s fraud detection, transaction monitoring, and risk-scoring workflows. Utilize modern software development practices and adhere to rigorous code review standards to ensure reliability, scalability, and maintainability.
• Collaboration with Cross-Functional Teams: Work closely with data science, product, and compliance teams to translate fraud detection models, AML/KYC rules, and risk policies into production-ready services and decision engines. Bridge the gap between theoretical models and practical implementation to enhance fraud detection capabilities.
• Data Architecture & Integration: Design and maintain scalable data architectures, including data pipelines, databases, and messaging systems, to support high-volume financial transactions and advanced fraud analytics. Integrate third-party AML/KYC, sanctions screening, and fraud platforms, as well as internal risk tools, into Rasēd’s ecosystem to create a cohesive and efficient fraud detection framework.
• Observability & Incident Management: Develop and maintain comprehensive monitoring, logging, and alerting systems for fraud-related services to ensure high availability, observability, and rapid incident response. Proactively identify and resolve issues to minimize downtime and enhance system reliability.
• Technical Leadership & Continuous Improvement: Participate in code reviews, technical design discussions, and architecture decisions to contribute to the continuous improvement of Rasēd’s platform. Stay abreast of emerging technologies in fraud detection, AI/ML, and software engineering, and propose innovative solutions to elevate Rasēd’s products and engineering practices.
• Client & Stakeholder Engagement: Collaborate with clients and internal stakeholders to understand evolving requirements, deliver tailored technical solutions, and support seamless deployments in production environments. Ensure alignment between technical capabilities and business objectives to drive customer satisfaction and operational excellence.

Qualifications & Competencies

• Education & Experience: Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field. A minimum of 2–5 years of professional software development experience is required.
• Technical Proficiency: Strong programming skills in at least one modern backend language, such as Java or .NET, with hands-on experience in building RESTful APIs or microservices. Proficiency in relational and/or NoSQL databases, data modeling, and performance optimization for high-volume systems is essential.
• Cloud & DevOps Expertise: Experience with cloud platforms (AWS, Azure, GCP, or OCI), containerization technologies (Docker), and orchestration tools (Kubernetes), along with CI/CD pipelines, to ensure scalable and efficient software delivery.
• Fraud & Risk Domain Knowledge: Familiarity with building data-driven or AI-enabled applications, such as rules engines, scoring systems, or anomaly detection, particularly in fraud and risk contexts, is highly desirable.
• Software Engineering Best Practices: A solid understanding of software engineering best practices, including testing methodologies, version control (Git), documentation, and secure coding, to ensure the delivery of high-quality, secure, and maintainable software solutions.