PNSY Ramith

Designed and implemented high-performance microservices using Java 17, Spring Boot, and Spring Cloud Gateway, handling over 5 million API requests per day across distributed systems. Engineered an AI testing framework to validate LangChain-based connectors, automate LLM prompt testing, and perform response consistency scoring, achieving 85% AI reliability coverage. Integrated Kafka Streams for asynchronous communication with at-least-once delivery semantics, improving system throughput by 60%. Developed and optimized AWS Lambda-based connectors using the AWS SDK, reducing cold start latency by 40%. Implemented distributed tracing pipelines using OpenTelemetry and CloudWatch for enhanced observability and debugging.

Designed and developed distributed microservices using Java 11, Spring Boot, Spring Cloud, and Kafka, processing over 10 million requests per day for dealer management systems. Built a DB Migration Service with Spring Batch and Redis, enabling high-throughput data synchronization between MySQL and DynamoDB with 65% faster performance. Designed secure, low-latency cross-cluster communication using JWT and OAuth2, optimizing service response time by 35%. Deployed microservices on Docker and Kubernetes (EKS), monitored with Prometheus and Grafana, which improved system uptime by 40% and sustained over 1 million concurrent event loads, verified through JMeter and Gatling performance testing. Engineered an AI-driven Alert Management System using Java 11, Spring Boot, Spring Cloud Gateway, Kafka Streams, and Elasticsearch to process and monitor 300K+ real-time events per day with predictive anomaly detection and intelligent alerting. Integrated an AI testing layer to validate inference accuracy and reliability using JUnit5, Mockito, and Testcontainers, achieving 90% automation coverage.

Designed and developed high-performance microservices using Java 17, Spring Boot, and AWS services, handling millions of real-time transactions per day with low latency and 99.99% uptime. Implemented event-driven architectures using Kafka and SQS, optimized interactions with DynamoDB and Aurora, and automated CI/CD pipelines, reducing release times by 40%. Engineered AI validation frameworks for recommendation engines, automating machine learning output verification and improving reliability by 30% across distributed systems. Built scalable, fault-tolerant distributed services using Spring Cloud and AWS, ensuring seamless operation under high load.