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Tier-1 brand, popular backend title, and Bengaluru metro raise applicant density despite seniority and specialization.
Requires specialized fraud and ML infrastructure experience, though distributed systems skills remain transferable across industries.
Explicit 13+ years requirement plus mandatory distributed systems, streaming, Java, and ML productionization increases filter strictness.
Lead architecture, design, and development of scalable fraud detection and risk evaluation data and ML infrastructure.
Build and optimize large-scale batch and real-time data pipelines with low latency and high reliability to support fraud and trust systems.
Own end-to-end productionization of ML models ensuring performance, scalability, reliability, and alignment with strict business SLAs.
13+ years experience in backend engineering, data engineering, or large-scale distributed systems development.
Strong experience with real-time distributed data processing systems and streaming technologies (e.g., Apache Flink, Kafka, Spark Streaming).
Proficiency in Java backend development and distributed system architecture.
Work Model: Hybrid with minimum 3 days onsite per week (office location requirements apply).
Experienced leader in designing and operating large-scale, real-time data platforms bridging Data Science and Engineering.
Domain knowledge in fraud detection, risk systems, or payments risk within commerce or fintech contexts is preferable.
Proven ability to drive technical vision, mentor senior engineers, and manage cross-functional initiatives for high scalability and operational excellence.