Alekhya Patcha

Designed and implemented Python backend services using FastAPI, Django REST Framework (DRF), and Ariadne (GraphQL) to power event-driven payment workflows, reducing manual transaction processing by 50% and enabling ML-driven fraud detection pipelines. Developed React dashboards with TypeScript, Redux, and Tailwind CSS, providing real-time analytics and AI-powered anomaly detection, improving investigation efficiency by 35%. Built a scalable microservices stack with Docker, Kubernetes (AWS EKS), and Helm to support high-volume financial data pipelines with 99.9% uptime. Engineered RESTful and GraphQL APIs integrating Stripe Radar, Sigma, and third-party services, enforcing OAuth2/JWT authentication for secure, compliant transactions. Managed PostgreSQL and MongoDB for transaction data and fraud-rule storage, improving query performance by 40%. Implemented asynchronous event-driven systems with Kafka, Kafka Streams, Celery, and Redis Streams to process millions of transactions in real ti

Engineered scalable backend APIs using Python (FastAPI/Flask) with REST and GraphQL endpoints powering ML inference services and edge device integrations. Built modern web frontends with React.js, TypeScript, Redux, and Tailwind CSS, delivering real-time dashboards for gesture recognition and IoT data visualization. Designed secure authentication flows with OAuth2, JWT, and Azure Active Directory for enterprise-grade identity management. Optimized data storage using PostgreSQL, MongoDB, and Azure CosmosDB, ensuring high availability for structured and unstructured telemetry data. Implemented WebSocket and SignalR-based real-time communication between edge devices and dashboards. Automated deployments with Docker, Kubernetes (AKS), Azure DevOps pipelines, and GitHub Actions for CI/CD and zero-downtime releases. Leveraged Azure Cloud Services (Functions, Blob Storage, IoT Hub, Service Bus) to handle distributed event-driven workflows and big data ingestion from edge devices. Implemented