Shravya Perala

Engineered and delivered a Store Inventory Intelligence System using Java 21, Spring Boot, React.js, and Kafka; ML-based demand forecasting reduced out-of-stock incidents by 18% and delivered measurable cost savings across a large-scale retail network. Designed distributed microservices with Spring Cloud, gRPC, Redis, and Resilience4j; instrumented with Prometheus, Grafana, and OpenTelemetry for observability and high availability under peak load. Built real-time inventory data pipelines using PySpark, Kafka, Pandas, and BigQuery; enabled live stream processing reducing excess inventory by 12% and improving stock turnover by 15%. Implemented LLM-powered anomaly detection using OpenAI API, LangChain, and RAG pipelines; reduced manual review time by 35% and improved incident response by 40%. Led CI/CD automation with Jenkins, Terraform, ArgoCD, and Kubernetes; supported blue-green deployments and monitored rollout health via Datadog and distributed tracing; reduced release cycles to unde

Developed enterprise-grade backend systems using Java 7, Spring Boot, Spring MVC, and REST APIs on Oracle Cloud Infrastructure (OCI), supporting 500+ global enterprise clients. Optimized Oracle 19c database performance via SQL/PL-SQL tuning, partitioning, and execution plan analysis, improving query performance by 20–25% on 50M+ records and reducing compute costs by 15%. Built event-driven microservices using Apache Kafka and Spring Cloud Stream, reducing inter-service coupling and improving throughput by 12%; integrated ELK Stack for centralized log aggregation and service health monitoring. Secured APIs using OAuth 2.0, OIDC, and Spring Security, enabling SOC 2 Type II readiness. Automated CI/CD pipelines with Jenkins, Docker, Kubernetes (EKS), reducing deployment time by 40% and failure rate by 30%, maintaining 85%+ code coverage. Delivered 2 major enterprise releases within Agile/Scrum environments, ensuring zero critical production defects and meeting strict SLA commitments.