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Tier-1 brand, metro location, and common backend title increase qualified applicant density despite seniority.
Strong fraud, ML operationalization, and large-scale streaming expertise limits cross-industry transferability.
Mandatory 13+ years, specific distributed systems, streaming and Java expertise, and ML production experience required.
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Lead architecture and development of scalable data and ML infrastructure for fraud detection and risk evaluation.
Build and optimize large-scale batch and real-time data pipelines and own ML model productionization ensuring performance and scalability.
Define technical vision, lead cross-functional initiatives, mentor senior engineers, and drive engineering standards for fraud prevention platforms.
13+ years of experience in backend engineering, data engineering, or developing large-scale distributed systems.
Strong experience with real-time distributed data processing systems and data streaming technologies like Apache Flink, Kafka, or Spark Streaming.
Proficiency in Java backend development and system architecture; experience deploying and scaling ML models in production.
Must be able to work onsite at least 3 days per week (Hybrid work model).
Experienced in building high-throughput, low-latency data pipelines and ML operationalization at scale.
Experienced partnering with Data Science teams to translate complex ML models into production-grade systems.
Background or interest in fraud detection, risk systems, marketplace, fintech, or payments domain.