• Led high-level & low-level system design (HLD/LLD) and full-stack Java Spring Boot development of microservices to perform efficient CRUD operations on PayPal business account and merchant profile data stored in Oracle DB — directly contributing to 75% user growth and $8M annual revenue growth across PayPal's core merchant systems

• Owned end-to-end PayPal's Blockchain & Cryptocurrency platform (PYUSD) — drove HLD/LLD system design, full-stack Java Spring Boot microservices development, Oracle DB integration, and rigorous testing, enabling users to transact with cryptocurrency at checkout and execute cross-border transfers, contributing to $7.82B in total transaction revenues

• Built Java Spring Boot + Spring Batch microservices integrated with Apache Kafka for event-driven distributed data processing on AWS (ECS/EKS) using Docker and Kubernetes (K8s)

• Managed CI/CD pipelines (Jenkins, GitHub Actions, Maven, Gradle); implemented real-time observability dashboards using Elasticsearch and Kibana for incident detection and operational excellence

• Designed REST APIs and GraphQL endpoints with Spring Security, OAuth2/JWT, and IAM-compliant authorization; applied cloud-native architecture across all distributed microservices

• Leveraged Generative AI tools — Claude Code, MCP Servers, Cursor, and Agentic AI — to accelerate development velocity and code quality in production workflows

• Mentored junior engineer; collaborated cross-functionally in Agile/Scrum — earned Bravo "Customer Champion" and "Win Together" awards

Tech stack: Java · Spring Boot · Spring Batch · Microservices · HLD/LLD · Blockchain · Cryptocurrency · PYUSD · Apache Kafka · Oracle DB · Docker · Kubernetes · AWS · REST APIs · GraphQL · Spring Security · OAuth2 · JWT · IAM · CI/CD · Jenkins · GitHub Actions · Datadog · Elasticsearch · Kibana · Generative AI · LLMs · Agentic AI · MCP Servers · Claude Code · Distributed Systems · Cloud-Native · Agile · Scrum

• Developed a high-performance Java Spring Boot payment refund application using microservices architecture that processes refunds for 134M+ active eBay buyers in cases of order cancellations or undelivered items — achieving 70% increase in system performance.

• Architected end-to-end refund processing system with Apache Kafka event-driven messaging, Oracle Database, Docker, and Kubernetes (K8s) deployed on AWS; implemented distributed tracing, real-time monitoring, and alerting for 24/7 operational excellence and cloud-native reliability

• Engineered production ML/AI pipelines using LLMs, BERT (NLP-based return reason classification), and XGBoost (fraud detection scoring) — eliminated 90% of buyer-seller disputes, strengthened customer trust, and reduced fraud-related operational costs across the platform

• Built REST APIs and GraphQL services for real-time refund status tracking; applied Spring Security with OAuth2/JWT for secure, IAM-compliant buyer data access; implemented CI/CD pipelines (Jenkins, GitHub Actions) for automated testing and deployment to AWS environments

• Performed SQL query optimization and data analysis on large Oracle DB datasets to ensure data integrity, reduce latency, and drive product insights for eBay's buyer protection program

• Led cross-functional collaboration with product, QA, and data science teams through full SDLC in Agile/Scrum; leveraged Generative AI and cloud-native development tools to accelerate delivery

Tech stack: Java · Spring Boot · Microservices · Python · BERT · XGBoost · LLMs · Fraud Detection · AI/ML · Apache Kafka · Oracle DB · Docker · Kubernetes · AWS · REST APIs · GraphQL · Spring Security · OAuth2 · JWT · IAM · CI/CD · Jenkins · GitHub Actions · Distributed Systems · Cloud-Native · Observability · Agile · Scrum · Payment Systems · Refund Processing

• Designed and developed a full-stack workflow case management tool with microservices architecture for customer service agents to handle complaints and escalations — generating $6–7M in annual business value.

• Built responsive front-end components using React.js, Redux, and Node.js; developed back-end REST APIs and microservices using Java Spring Boot — delivering end-to-end SDLC from requirements through production deployment

• Applied Machine Learning models (Python, Scikit-learn, Pandas, NumPy) to automate complaint resolution workflows — reducing investigation complexity, generating $3–3.8M in operational savings and $3M in customer revenue annually while enforcing government regulatory compliance

Implemented CI/CD pipelines

• Collaborated with product managers and senior stakeholders in Agile/Scrum; recognized with Bravo "Innovation – We Dare" badge for delivering AI-driven automation solutions

Tech stack: Java · Spring Boot · React.js · Redux · Node.js · Python · Machine Learning · Scikit-learn · Pandas · NumPy · Microservices · REST APIs · CI/CD · Regulatory Compliance · Case Management · Agile · Scrum

• Designed and built enterprise case management systems and a real-time monitoring framework from scratch — automating 50+ manual tasks, improving Average Handle Time (AHT) by 80%, and saving $756K–$1.2M annually.

Case Management Systems (KYC/CIP & Merchant Onboarding):

• Designed and built full-stack Java Spring Boot + React.js/Node.js case management systems to automate merchant onboarding, CIP/KYC verification, account verification, and vetting workflows — enabling faster customer and merchant onboarding across PayPal's platform

• Engineered GraphQL APIs and RESTful microservices with Oracle Database; implemented Spring Security with OAuth2/JWT for IAM-compliant secure authentication and authorization; automated 50+ manual review tasks, improving AHT by 80%

• Mentored two Software Developers; collaborated cross-functionally with senior stakeholders in Agile/Scrum — awarded Bravo "One Team – Collaboration" badge

Real-Time Monitoring Framework (Kafka Streams / Elasticsearch):

• Architected end-to-end Real-Time Monitoring Framework tracking health, availability, performance, logs, and business analytics of customer service applications and Oracle WebLogic infrastructure

• Built distributed backend microservices using Java Spring Boot and Apache Kafka — engineered the first-ever Dynamic Kafka Stream Processor at PayPal capable of modifying stream processing and aggregation logic at runtime, eliminating reliance on third-party monitoring tools and saving $756K–$1.2M annually

• Implemented Quartz Scheduler for metrics collection; built business metrics visualization dashboards using Elasticsearch and Kibana; integrated Hadoop and HiveQL for data processing at scale

Tags: Java · Spring Boot · React.js · Node.js · Redux · Apache Kafka · Kafka Streams · Oracle DB · Hadoop · HiveQL · Elasticsearch · Kibana · GraphQL · REST APIs · OAuth2 · JWT · IAM · KYC · CIP · Merchant Onboarding · Quartz Scheduler · Distributed Systems · Microservices · Agile · Scrum

• Researched data streaming platforms and developed a Java-based prototype for the Real-Time Monitoring Framework using Apache Kafka Stream Processor; documented implementation procedures and informed architectural decisions for the production system

• Built a health-check microservice in Node.js, persisting health data on MySQL, providing baseline infrastructure observability for PayPal's customer service application stack

Tech stack: Java · Apache Kafka · Kafka Streams · Node.js · MySQL · Microservices · Real-Time Monitoring · Distributed Systems