Java

Overview I design and build backend systems that scale. From Spring Boot microservices architectures processing thousands of events per minute to distributed systems with service discovery, circuit breakers, and event-driven communication - I deliver server-side infrastructure that is maintainable, observable, and production-ready. What I Can Do For You Microservices Architecture Service decomposition: Break monoliths into independently deployable services with clear domain boundaries Service discovery and configuration: Eureka, Spring Cloud Config for dynamic registration and centralized configuration API Gateway: Spring Cloud Gateway for routing, rate limiting, authentication, and request transformation Resilience patterns: Circuit breakers (Resilience4j), retry logic, bulkheads, and graceful degradation Distributed Systems Inter-service communication: REST, gRPC, or event-driven via message queues Distributed tracing: End-to-end request tracing across service boundaries for debugging and performance analysis Event-driven architecture: SQS, RabbitMQ, or Kafka for asynchronous, decoupled workflows Real-world example: AI Sports Match Prediction - Spring Boot microservices handling multiple sports leagues, 60+ feature ML models, and real-time prediction updates Backend Development RESTful and GraphQL APIs in Java, TypeScript, or Python Database design: Relational (PostgreSQL, MySQL, MSSQL) and NoSQL (DynamoDB, MongoDB) Background processing: Batch jobs, scheduled tasks, and streaming pipelines Testing and observability: Unit/integration tests, structured logging, metrics, and alerting Technologies Frameworks: Spring Boot, Spring Cloud, NestJS Patterns: Eureka, API Gateway, Circuit Breaker (Resilience4j), distributed tracing Messaging: SQS, RabbitMQ, EventBridge Databases: PostgreSQL, MSSQL, DynamoDB, MySQL Languages: Java, TypeScript, Python

An AI-powered sports match prediction platform developed for a leading Turkish sports analytics company. The system leverages machine learning models and microservices architecture to deliver data-driven match predictions, statistical analysis, and real-time insights across multiple sports leagues. Architecture: Microservices with Spring Boot The platform was built using a microservices architecture with Java Spring Boot, enabling independent deployment, scaling, and maintenance of each domain-specific service. Core Microservices: Match Data Service: Ingests and normalizes match data from multiple league APIs (football, basketball, tennis, esports) Prediction Engine Service: Runs ML models to generate match outcome probabilities, score predictions, and trend analysis Statistics Service: Computes real-time team/player statistics, head-to-head records, form analysis, and historical performance metrics User Preferences Service: Manages user follow lists, notification preferences, and personalized prediction feeds Notification Service: Delivers push notifications for match start times, live score updates, and prediction alerts AI/ML Integration The prediction engine combines multiple approaches: ...