Ramesh Reddy Changal

Designed and developed scalable telecom-focused microservices using Java 17, Spring Boot, and REST APIs, embedding AI/ML intelligence across the stack. Built LangChain and ChromaDB-based RAG pipelines for subscriber query resolution, integrated Vertex AI for predictive engagement analytics, and deployed LLM-powered automation workflows with Python FastAPI and LangGraph. This work reduced support ticket volume by 30% and improved customer satisfaction. Implemented end-to-end AI/ML pipelines (Pandas, NumPy) for feature engineering and model evaluation (Scikit-learn, Hugging Face Transformers) achieving 87% churn prediction accuracy and 91% network anomaly precision. Connected Kafka and Google Pub/Sub for real-time data streaming between OSS/BSS and media applications; containerized services on AWS Fargate with auto-scaling; set up GitLab CI/CD and model versioning/monitoring in production.

Led migration from monolith to microservices with Java 17, Spring Boot, Spring Cloud, and REST APIs for enterprise customer management and billing, incorporating Python FastAPI inference services for billing anomaly detection. Optimized backend with Spring MVC/Hibernate/JPA, improving transaction throughput by 35% and reducing API response times by 30-40%. Built Angular SPAs (10-11) and integrated Kafka/RabbitMQ for reliable asynchronous messaging. Implemented AWS Lambda-based ML inferences and internal ML pipelines, reducing manual processing by 40%. Established API gateway security, rate limiting, and CI/CD pipelines via Docker, Kubernetes on AWS EKS, and Secrets management with AWS Secrets Manager and HashiCorp Vault, helping achieve SOC 2 compliance.

Modernized legacy banking applications by decomposing into Java 8/Spring Boot microservices deployed on AWS Elastic Beanstalk and EC2, with Oracle and PostgreSQL backends. Implemented event-driven processing using Kafka, Redis Streams, and RabbitMQ to support real-time payments and alerts; built SPAs with Angular 6-7 and TypeScript. Implemented AWS Lambda-driven workflows for loan processing and reconciliations; secured applications with Spring Security OAuth2/JWT and RBAC; designed Spring Batch ETL pipelines for nightly reconciliation and regulatory reporting. Deployed API Gateways and comprehensive CI/CD with Jenkins/GitHub Actions; containerized services with Docker and Kubernetes for scalable, fault-tolerant operation.