We are looking for a Backend Engineer to design, build, and own real-time Fraud Risk Management (FRM) systems that evaluate every transaction for fraud and AML risks under strict sub-50ms latency SLAs. The role requires strong proficiency in Python and working experience with GoLang to build high-performance, low-latency backend services. You will design scalable streaming and event-driven systems using Kafka, build efficient algorithms and data structures for real-time decisioning, and optimize PostgreSQL, Redis, and Pinot for fast lookups and aggregations. The role involves deep focus on performance, correctness, idempotency, and reliability, with close collaboration across Risk, Compliance, and Product teams to translate policies into deterministic, production-ready systems running on AWS.
Technical Skill Set Requirements
1. Programming Languages
• Strong proficiency in Python for production-grade backend services.
• Working experience with GoLang for high-performance components.
• Strong understanding of data structures and control flows.
2. Algorithms & Data Structures
• Strong problem-solving ability.
• Designing and implementing low-latency algorithms.
• Choosing optimal data structures for real-time decisioning and aggregation.
3. Streaming & Event Processing
• Experience with Kafka for real-time event ingestion and processing
• Understanding of streaming semantics, ordering, retries, and idempotency
• Designing systems for near real-time fraud detection and rule execution
4. Databases & Storage
• Strong proficiency in PostgreSQL
• Query optimization for low-latency lookups and aggregations
• Experience with Redis for caching, counters, and fast state access
• Experience with Apache Pinot or similar OLAP stores for real-time analytics and aggregations
5. Performance & Low Latency Systems
• Designing systems that consistently respond under strict latency SLAs (sub-50ms)
• Efficient use of caching strategies, in-memory computation, and pre-aggregation
• Profiling and optimizing code paths for CPU and memory efficiency
6. System Design
• Designing scalable, reliable, and fault-tolerant real-time systems
• Handling high-throughput transaction processing with consistency guarantees
• Designing for horizontal scalability and graceful degradation
7. Cloud & Infrastructure
• Experience with AWS services for deploying and operating distributed systems
• Understanding of networking, autoscaling, and high-availability patterns
8. Engineering Practices
• Strong unit and integration testing mindset
• Experience with production debugging and performance tuning
• Code reviews and design documentation
Role & Responsibilities
• Design, build, and own real-time Fraud Risk Management (FRM) systems that evaluate every transaction and identify fraud and AML risks
• Implement low-latency fraud detection and rule execution pipelines with strict sub-50ms response time SLAs
• Design and optimize streaming workflows for real-time ingestion, aggregation, and decisioning
• Build and optimize efficient algorithms and data structures for fraud scoring and rule evaluation
• Leverage caching, pre-computation, and in-memory strategies to meet performance requirements
• Design and optimize PostgreSQL, Redis, and Pinot data models for fast lookups and aggregations
• Ensure correctness, consistency, and idempotency in real-time transaction processing
• Work closely with Risk, Compliance, and Product teams to translate fraud and AML policies into deterministic and testable rules
• Monitor, debug, and improve system performance and reliability in production
• Write clean, well-tested, and maintainable code in Python and GoLang following best engineering practices
Eligibility & Qualifications
• Bachelor’s degree in computer science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• A master’s degree is a plus.
• 4 + Years of experience
