Requirements

Strong experience building and operating production systems on Java/JVM with expert-level technical knowledge.
Solid understanding of distributed systems and event-driven architectures (Kafka or similar), designed for low latency, high availability, and scale.
Proven track record in technical leadership on large impactful projects, managing design, RFCs, delivery, and long-term operation.
Experience designing systems for long-term iteration and scaling with well-documented API-first interfaces and proactive technical debt management.
Accountability for system reliability, security, and observability, including incident resolution and post-incident reviews.
Product mindset with ability to articulate customer problems solved and impact, and to provide constructive feedback on product roadmap.
Experience mentoring and coaching engineers to raise engineering quality through code reviews, design discussions, and best practices.
Excellent communication skills for collaboration across Product, Data Science, Fraud Operations, and Analytics teams.

Nice to Haves

Experience with Python, machine learning concepts, deploying models in production, or working with large language models in production.

Shape and build systems that prevent fraud and scams via real-time transfer and profile decisioning.
Own the technical roadmap and deliver key prevention domain projects with clear customer impact.
Develop deep expertise in technical and fraud domains, improving latency, reliability, and decision quality of the platform.
Address technical debt, maintain risk within appetite, and enforce engineering best practices such as observability, security, Kafka/DB usage, and testing.
Lead engineering efforts including mentoring, knowledge sharing, onboarding, and interviewing new talent.
Contribute to cross-team collaboration efforts, guilds, and reduce duplicated work across fraud and risk domains.