Krishna Reddy

Built and optimized scalable workflows for legal-request processing, significantly reducing processing time and manual review effort. Implemented cross-matter fuzzy matching to improve candidate selection and introduced idempotent, asynchronous processing to ensure high audit-event capture. Supported production operations by investigating Kafka/JMS incidents with log and message tracing and applying fixes with automated regression coverage.

Improved commerce workflow performance by enhancing configurable quote generation and order submission logic, increasing conversion and revenue outcomes. Developed AI-assisted quoting experiences using Angular and TypeScript with REST-based integrations for real-time pricing and quote summaries. Added reliable CI/CD-delivered testing coverage using Jenkins and unit test frameworks.

Designed and delivered Java/Spring Boot microservices and REST APIs supporting large-scale enterprise transaction processing. Used Maven for builds and Docker/Kubernetes for containerized deployments and operational readiness across integrated systems. Improved reliability by adding resilient validation, retries, and error-handling patterns to reduce integration failures and production support volume.

Implemented customer-facing learning features for a scalable digital learning platform. Built interactive and gamified experiences using Java, Spring Boot, and a React-based front end to improve learner outcomes. Focused on delivering usable UI components with standard web technologies and styling frameworks.

Built a Flask-based EV charger queue prototype to manage check-in and charging slot availability. Added QR-based entry, FIFO waitlisting, timed holds, automated reminders, and automatic release for overstays. Used a SQLite-backed design to track queue state and handle notifications via pluggable channels.

Developed an end-to-end retrieval-augmented question answering pipeline for legal documents. Integrated LangChain with OpenAI and a vector database for retrieval, and exposed the service via FastAPI. Deployed an AWS Lambda-based backend to serve low-latency responses.