Requirements

4+ years of experience with Java, Kotlin, or other JVM-based languages, with deep knowledge of the Spring framework.
Obsession with latency: ability to optimize throughput and speed, keeping p99 end-to-end latency under 1 second for real-time checks at high transaction volumes.
Deep understanding of JDBC, database internals, and query optimization.
Hands-on experience with Kafka, Kafka Streams, or Apache Flink for real-time event processing.
Experience with CI/CD pipelines and designing scalable, resilient distributed systems.
Product and user focus: prioritizing customer needs and improving UX by using data to fix pain points like false positives.
End-to-end project ownership from idea to production monitoring.
Strong communication skills to explain complex technical concepts to non-technical audiences.
Experience collaborating daily with Data Scientists and Data Analysts to deploy models and refine rule sets.

Nice to Haves

Familiarity with machine learning basics including data pipelines, feature engineering, and model metrics like precision/recall.
Experience with OLAP databases or data lakes (e.g., Apache Iceberg) and Kafka ingestion.

Building and maintaining real-time risk assessment systems leveraging machine learning for instant decision making during customer transactions.
Owning the risk infrastructure for Instant ACH, enabling instant transfers despite payment network limitations.
Collaborating closely with Software Engineers, Data Analysts, and Data Scientists to design new rules, improve ML models, and support fraud investigations.
Expanding risk coverage to include new types such as Sanctions screening and Anti-Money Laundering for the US and Canada.
Contributing to operational excellence with a "you build it, you run it" approach to ensure robust, reliable systems.
Building strong global relationships within the engineering tribe and with US stakeholders to deeply understand feature "why."