Designed and optimized REST and GraphQL APIs toprocess fraud indicators, reducing latency by 40% and enabling realtime scam detection workflows that prevented thousands of fraudulent transactions monthly.

Implemented API versioning, rate limiting, and token-based authorization with AWS API Gateway and OAuth 2.0/JWT, ensuring secure data exchange with zero security breaches.

Refactored monolithic fraud detection logic into modular Java Spring Boot microservices with AWS Lambda, DynamoDB, and API Gateway, reducing overhead by 35% and improving response time by 25%.

Built CI/CD pipelines via Jenkins and GitHub Actions, automating deployments across environments and cutting release time from hours to under 15 minutes.

Integrated canary and blue/green deployments with auto-rollback triggers for safe fraud detection updates.

Managed secure identity flows using RBAC, refresh tokens, and encrypted token storage to protect sensitive fraud intelligence data.

Designed PostgreSQL schemas with Redis caching and stored procedures for real-time fraud analytics ,improving query performance by 50%.

Containerized services with Docker and deployed to Kubernetes via Helm charts, configuring TLS ingress and traffic throttling for secure, scalable routing.

Ensured reliability through layered testing (unit ,integration, e2e) using Jest, Cypress, Playwright, and observability tools like AWS X-Ray, CloudWatch, and Log Insights.

Designed and maintained RESTful APIs for upstream/downstream data synchronization, integrating with third-party banking systems and payment gateways, reducing data transfer errors by 30%.

Engineered robust real-time messaging pipelines using Apache Kafka for inter-service communication and JMS for legacy banking interactions, increasing system throughput by 45%.

Refined and optimized MySQL and MongoDB schemas with indexes, stored procedures, and migration scripts, improving query performance by 60% and ensuring reliable transaction processing.

Containerized Java microservices using Docker and deployed on Azure Kubernetes Service (AKS), reducing deployment downtime by 70% through efficient orchestration.

Automated CI/CD pipelines via Azure DevOps, incorporating SonarQube for static code analysis and pre-deployment gates for quality checks, linting, and multi-environment deployments.

Built resilient backend integrations with Apache Camel, enabling seamless orchestration between ACBS/CLS banking platforms and internal systems.

Monitored microservices and API health using Azure Monitor, Application Insights, and custom logging pipelines, reducing mean time to resolution (MTTR) by 35%.

Implemented test automation suites using JUnit, Mockito, and Postman, achieving over 90% code coverage and reducing post-release bugs by 40%.

Participated in Agile ceremonies including sprint planning, code reviews, and retrospectives, ensuring on-time delivery of 95% of banking integration milestones.