1. Architected and built a real-time fraud detection and risk decision engine evaluating 200+ signals per transaction with sub-200ms latency at billion-transaction scale, using Spring Reactive and Redis

2. Designed and implemented fraud risk scoring systems to detect:

a. payment fraud

b. bulk order abuse

c. coordinated and repeat fraud patterns

3. Identified common links across fraudulent users (device, behavior, transaction patterns), enabling detection of fraud rings and large-scale abuse campaigns

4. Analyzed evolving attacker behavior and intent, continuously adapting detection logic to mitigate new fraud strategies and reduce system exploitation

5. Built internal fraud prevention platforms:

a. Trust Gate: Real-time enforcement system that evaluates transactions at the point of action and blocks high-risk activities (e.g., payments, orders) using fraud risk scoring and decision rules.

b. Trust Ramp: Periodically evaluates user behavior, telecom usage patterns, and risk signals to dynamically adjust trust levels and identify emerging fraud.

c. Breach Shield: Dark web monitoring and account protection system that detects exposed user credentials and compromised accounts, enabling proactive risk mitigation and account takeover prevention

6. Achieved 98.63% fraud detection accuracy through a combination of automated decisioning and human feedback loops

7. Built real-time observability and monitoring systems (Datadog dashboards) for fraud detection, compliance, and operational response

8. Led secure payment architecture initiatives (PCI DSS, SOC 2), integrating Stripe PaymentIntent and third-party risk systems

9. Collaborated with product, payments, and engineering teams to embed fraud controls into checkout, onboarding, and account flows

10. Mentored engineers, led architecture decisions, and contributed to backend platform strategy across teams

• Architected and implemented a microservices-based platform to process continuous IoT sensor data in real time using Spring Boot, WebFlux, and HiveMQ (MQTT).

• Designed and deployed scalable backend services with REST APIs, MySQL, and Redis caching, ensuring high reliability and maintainability across distributed rail operations.

• Engineered reactive streaming pipelines to handle terabytes of sensor data with sub-second latency, enabling predictive maintenance and improved operational safety.

• Delivered operator-facing dashboards with Angular & Bootstrap, reducing reporting and inspection times by 40%.

• Secured mission-critical APIs with Spring Security and JWT-based authentication, ensuring compliance with federal data protection standards.

• Introduced centralized observability with the ELK stack, reducing mean time to resolution (MTTR) for anomalies by 30% and improving system uptime.

• Migrating existing monolithic backend systems into Restful microservices using spring boot.

• Used Spring AOP for cross cutting concerns such as logging request/response payloads and global exception handling.

• Designed and implement access control layer using Spring Security and JPA authentication.

• Designed and implement multi-threaded and non-blocking function calls to send and fetch data from RabbitMQ.

• Used Spring Data JPA along with Hibernate for executing CRUD operations on Oracle and Postgres DB.

• Designed cost-efficient solution for application logs with AWS ELK stack.

• Configured logstash: input, filter, output plugins for collecting and shipping application logs to Elasticsearch.

• Elevate number of transactions per second by 10% using Amazon’s in memory cache service ElastiCache Redis.

• Increase load and functional scalability of microservices by 30% with Event-driven architecture design using Reactive Spring, Spring JMS and Amazon SQS (Simple Queue Service) as middleware.

• Enriched security posture of microservices with two-way authentication using Amazon API Gateway service.

• Enforced Test Driven Development through CI/CD pipelines using Gitlab, Jenkins, SonarQube and Jacoco for code coverage.

• Involved with Cloud Services and Platform Engineering team for research and design of Verizon Kubernetes infrastructure.

• Designed unified logging layer for Verizon Kubernetes infrastructure using Python Kubernetes client libraries, custom resource controller, and Fluentd.

• Integrate Twistlock in Kubernetes clusters for real-time application security scans and containers compliance policy enforcement.

• Examine overall security posture and Twistlock’s credibility through penetration testing for SQLInjection, Cross Site Scripting (XSS), Cross Site Request Forgery (CSRF) attacks.

• Designed utilities and tools for Kubernetes infrastructure using C++ and Python Flask to improve developer’s experience during application deployment.

• Created high-performance financial libraries and a rule-based engine in C++, improving processing throughput by 23%.

• Refactored legacy systems using asynchronous messaging using RabbitMQ, enabling non-blocking communication across services.

• Developed Batch Controller Utility in Python to distribute workloads across servers, scaling throughput for financial transactions.

• Configured Jenkins pipelines and enforced TDD with SonarQube, Jacoco, and coverage tools, raising code quality across teams.