Pavan Kalyan

Engineered AI-driven payment APIs and tokenization systems using Spring Boot, Hibernate, Python, and RESTful microservices; optimized API response times by 35%, supporting 30,000+ concurrent AI-initiated payments daily. Integrated Generative AI models and RAG pipelines (LlamaIndex) to autonomously verify transaction intent and reconcile contextual merchant data, improving fraud detection accuracy by 28% with sub-second responses. Implemented OAuth2/JWT-based authentication with MFA, achieving 99.99% uptime for AI-initiated transactions. Employed MongoDB and Redis to cache real-time transaction data and AI context, reducing latency by 40%. Orchestrated AI-driven transaction workflows using LangChain, automating payments and compliance checks and cutting manual intervention by 40%. Designed IaC pipelines with Terraform to provision AWS environments and automate CI/CD, reducing deployment times by 50%. Collaborated with Microsoft/IBM to standardize data contracts and ensure PCI DSS compli

Architected microservices for retail order processing using Java, Spring Boot, Spring Cloud, REST APIs, and Python automation, reducing latency by 42% and improving customer satisfaction. Containerized pipelines with Docker, Kubernetes, and Helm on AWS, achieving 99.98% uptime and auto-scaling during peak periods. Automated real-time Kafka event pipelines for inventory and pricing across 120+ stores, reducing mismatches by 35%. Migrated ETL from MySQL to PostgreSQL and integrated MongoDB, improving analytics latency by 50%. Integrated AWS serverless workflows (Lambda, S3, RDS) to streamline order processing and analytics, cutting infrastructure costs by 25%. Implemented end-to-end full-stack workflows with Spring Boot + React, boosting operational efficiency by 30%. Managed Jira workflows to improve sprint delivery by 25%. Built interactive React + TypeScript dashboards for KPIs, inventory alerts, and predictive insights, increasing actionable insights by 30%. Developed Python automati

Developed a secure mobile-optimized patient portal enabling access to medical records and appointment scheduling, boosting portal adoption by 45%. Integrated EHR with external diagnostic labs using HL7/FHIR APIs, ensuring real-time data synchronization and eliminating manual entry errors. Revamped the care management dashboard in React for real-time vitals monitoring across 4,000+ patients. Optimized medical claims processing by integrating insurer APIs, increasing first-pass acceptance from 78% to 96%. Implemented automated testing (JUnit, Mockito, Cypress, Postman/Newman) to improve defect detection and reduce production bugs by 35%. Built automated appointment reminders using Node.js and Python on AWS Lambda/S3/RDS, reducing no-shows by 22%. Implemented MLflow and SageMaker monitoring for predictive healthcare models to improve tracking and deployment of patient risk scoring.