Akhil Reddy Pabbathi Reddy

RoamSafe is a full-stack, cloud-native roadside assistance platform that connects stranded drivers with nearby service providers in real time. I led the end-to-end development of this smart service platform, integrating location services, live status tracking, and a scalable microservices back end to support dynamic service dispatching.

The front end, built with React and Tailwind CSS, enables users to request help and track mechanics' arrival quickly. In contrast, the back end, powered by Spring Boot and Node.js microservices, ensures low-latency request processing, secure messaging, and intelligent routing.

For high availability, I implemented AWS-based infrastructure using Amazon EC2, AWS Lambda, and Amazon S3 (AWS S3). I also leveraged containerized services with Docker and Kubernetes for scalability. The platform also features push notifications, authentication with OAuth 2, and real-time updates via WebSockets. This project demonstrated my ability to architect resilient systems, implement modern UI/UX, and orchestrate full-stack solutions with high production quality.

SentimentSphere is a scalable sentiment analysis platform built with Google Generative AI, designed for extracting real-time sentiment insights from structured and unstructured text data.

I led full-cycle development, from architecting a modular, cloud-ready solution to implementing efficient chunk-based processing for large datasets. The platform includes a secure, RESTful API layer using Flask, with dynamic data visualizations powered by Matplotlib to present sentiment distributions. Designed with fault tolerance and extensibility in mind, SentimentSphere can be easily integrated into enterprise pipelines like customer feedback systems or social listening tools. It supports .env-based configurations and is deployable on cloud platforms such as AWS and Heroku. Future enhancements include live sentiment tracking, multi-language support, and richer visual analytics. This project reflects my commitment to building AI-powered, production-ready systems that are adaptable to evolving business needs.

I led the E2E development of a scalable fraud detection system tailored for enterprise-level transaction monitoring across financial institutions. The solution was designed to identify anomalous behavior in real time while processing millions of records daily.

The project involved comprehensive data engineering, including ingestion of high-volume datasets, exploratory data analysis (EDA) to uncover hidden patterns, and feature engineering to optimize predictive accuracy. I built a robust machine learning pipeline using Scikit-Learn, integrating multiple classification models such as Random Forest, Logistic Regression, and Gradient Boosting. I also applied grid search and cross-validation techniques to fine-tune model hyperparameters, achieving a precision-recall balance critical for minimizing false positives in high-stakes financial environments. The final system included a modular, production-ready deployment strategy with model performance tracking.

I collaborated with a cross-functional team of data scientists, business analysts, and product stakeholders to align modeling outputs with actionable fraud insights. I deployed visual dashboards using Matplotlib and Seaborn for fraud pattern reporting and compliance visibility.