Built high-throughput payment microservices using Java 17, Kotlin, and Spring Boot 3, sustaining over 1,500 transactions per second at sub-50ms latency (p99), ensuring performance and reliability for mission-critical financial systems.

Designed and implemented scalable REST APIs with Spring Boot following cloud-native principles, improving integration with enterprise banking systems and increasing service scalability by 40%.

Integrated Kafka-based Change Data Capture (CDC) pipelines to stream real-time transactional database changes into a centralized data lake.

Developed a PCI DSS-compliant fraud detection pipeline using Kafka Streams and Spark Structured Streaming on Amazon EMR, reducing false positives by 23% and enhancing transaction monitoring accuracy.

Orchestrated containerized deployments using Docker and Kubernetes on AWS, managing autoscaling with Horizontal Pod Autoscaler (HPA) and Elastic Load Balancer (ELB) to handle variable workloads while maintaining high availability.

Automated CI/CD pipelines with Docker integration, reducing deployment time by 30% and ensuring consistent builds and zero-downtime rollouts across development, QA, and production environments.

Designed and implemented React.js dashboards integrated with GraphQL APIs and led efforts in documenting design diagrams and runbooks for payment microservices.

Enhanced Spring Boot 3 microservices for fault tolerance by applying resilience patterns (circuit breaker, retries), reducing downtime during high-traffic events.

Tokenized and encrypted sensitive payment data using AWS Key Management Service (KMS) and HashiCorp Vault, passing internal and external security audits with zero critical vulnerabilities.

Built batch and real-time ETL pipelines using ETL pip (SQL & DataFrames) and EMR Serverless to process large-scale CDC data, transforming it into analytics-ready formats for regulatory and BI reporting.

Architected distributed AWS infrastructure using EC2, S3, and CloudFormation templates, achieving 95% uptime and aligning with cloud security best practices and compliance mandates.

Led back-end code standardization initiatives by conducting regular code reviews, defining Kotlin style guides, and mentoring junior developers, resulting in a 40% reduction in pull request cycle time.

Refactored legacy JDBC layers with Spring DAO and JDBC Templates, optimizing SQL queries for DB2 databases and improving database response times by up to 25% in data-intensive workflows.

Implemented data orchestration workflows using Apache Airflow DAGs to monitor, schedule, and manage complex multi-step data pipelines, ensuring reliability and transparency in data processing.

Exposed real-time financial metrics via GraphQL APIs to React + TypeScript dashboards, enabling risk analysts to monitor transaction health and performance with second-by-second visibility.

Enhanced legacy web applications by migrating Angular 2/4 components to Angular 9/11 with TypeScript, improving maintainability, reducing runtime errors, and decreasing page load times for customer-facing portals.

Developed and deployed microservices using Spring Boot and Spring Security on AWS EC2 and Pivotal Cloud Foundry.

Designed real-time Spark Streaming applications in PySpark to process event data from Apache Kafka.

Engineered AWS-native reconciliation microservices using Spring Boot, Kafka, and DynamoDB to balance over 10 million ACH and card transactions daily with high accuracy and fault tolerance.

Built scalable ETL pipelines using Apache Spark on EMR and integrated with S3.

Contributed to Spring Boot microservices integration with AWS-native services (S3, DynamoDB, Lambda).

Integrated observability stack using Prometheus, Grafana, and ELK, defining latency/error SLOs and reducing MTTR from 45 to 12 minutes through proactive alerting and metrics visualization.

Developed and productionized fraud-scoring ML models using Amazon SageMaker and served them via Kafka Streams.

Implemented serverless workflows by integrating Python-based AWS Lambda functions with Step Functions to automate post-processing and data validation tasks for batch pipelines.

Migrated legacy JavaScript and Java modules to TypeScript and Kotlin, reducing debugging time by 30% and improving code maintainability across both front-end and Android applications.

Developed and deployed Spring Boot batch jobs on Red Hat OpenShift and Linux batch servers for large-scale data processing and reporting across distributed environments.

Leveraged Kubernetes and Docker to containerize microservices, manage CI/CD pipelines, and improve runtime consistency across testing and production environments.

Led CI/CD pipeline implementations using Maven, Bitbucket, SonarQube, JFrog, Bamboo, and Jira for Angular apps.

Created HTML, CSS, JavaScript, and DHTML pages for a presentation layer.

Used Spring framework for implementing Spring IoC, JDBC, and ORM.

Built and tested Spring Boot services for back-end modules and implemented REST API integrations with Struts and Spring.

Developed code changes using Core Java and J2EE technologies and used Eclipse, JDBC, and MySQL.

Wrote SQL queries and executed using JDBC API and used Servlets to maintain server-side business logic.

Used Maven to build and deploy the application on WebLogic Server.

Contributed to early React.js adoption by modernizing presentation-layer modules, reducing maintenance effort for UI changes.