Easebuzz- Backend Engineer (FRM)
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
