CHARITH PRODDUTURI

Summary

Implemented a Python-based RAG (Retrieval-Augmented Generation) system utilizing OpenAI API and Lang Chain framework to enable semantic search across GitHub repositories. Engineered Git integration module using the subprocess library to efficiently clone and extract repository metadata, supporting 30+ file types across multiple programming languages. Developed a document processing pipeline with Recursive Character Text Splitter for optimal chunking (3000-character chunks with 200-character overlap) and NLTK for tokenization and text normalization. Built a hybrid vector search engine combining BM25Okapi and TF-IDF with cosine similarity (0.5/0.5 weighting) to improve semantic relevance in document retrieval. Integrated LLM using Lang Chain's Prompt Template and OpenAI's GPT models, with conversation history management for contextually relevant responses to repository-specific technical queries. Implemented scalable state management and multi-user interaction using Dash callbacks and Flask sessions for collaborative research workflows. Developed backend services with Fast API and Flask to support ML model inference, data preprocessing, and metadata management. Designed and integrated APIs using Fast API and Open API for seamless interaction between research databases, ML pipelines, and external tools.

Summary

Leveraged SQL queries and PostgreSQL schemas to enhance database performance for research-driven projects and student assignments related to distributed systems and database optimization. Coordinated in configuring CI/CD pipelines using Jenkins and GitLab to demonstrate software deployment automation concepts in advanced operating systems coursework. Facilitated cloud computing demonstrations, guiding students in deploying containerized applications and exploring AWS-based infrastructure for scalable operating system solutions. Explained machine learning models for predictive analytics, illustrating uses in system performance optimization and resource allocation. Designed NLP models for text processing and sentiment analysis, assisting in research and student projects related to automated system monitoring and log analysis.

Summary

Designed and developed responsive web applications using React.js, Angular 14, JavaScript, and Bootstrap, focusing on performance optimization, accessibility, and cross-browser compatibility to deliver a seamless user experience. Built and maintained backend microservices using Spring Boot and Java, implementing business logic and integrating with RESTful APIs and NoSQL databases (MongoDB) to ensure efficient data management and high system availability. Automated end-to-end testing using JUnit, Mockito, increasing test coverage by 40% and reducing regression bugs in production. Developed CI/CD pipelines using Jenkins, Maven, and Git, enabling automated build, test, and deployment processes across development, staging, and production environments. Deployed and managed containerized applications using Docker and supported migration to cloud-based infrastructure using AWS (Lambda), enhancing system scalability, uptime, and cost-efficiency. Participated in daily stand-ups, sprint planning, and code reviews as part of an Agile Scrum team, collaborating closely with product managers, QA engineers, and DevOps teams to deliver high-quality, production-ready features on time. Implemented logging and monitoring solutions using AWS CloudWatch, proactively identifying performance bottlenecks and reducing mean time to recovery (MTTR) during incidents. Documented technical specifications, created user guides, and mentored junior interns, contributing to knowledge sharing and improving team onboarding processes.

Summary

Designed and implemented scalable backend modules in C++ to handle high-concurrency booking requests, reducing average API response time by 30% and supporting peak traffic of over 100,000/day. Engineered interactive, component-driven UIs using Angular, enabling seamless search filters and dynamic calendar features, resulting in a 20% increase in user engagement and booking conversions. Developed and optimized complex stored procedures and triggers using Oracle, enhancing data retrieval efficiency for booking history and payment systems while maintaining ACID compliance across multi-tenant databases. Built and deployed stateless microservices using AWS Lambda, configured object storage with S3 for customer assets, and provisioned autoscaling infrastructure on EC2, achieving an uptime and cost optimization. Applied Machine Learning models and Natural Language Processing (NLP) techniques to classify user reviews, detect fraud patterns, and personalize user recommendations, increasing trust and relevance in search results. Managed collaborative development using GitHub, leading code reviews, pull requests, and CI/CD pipelines, which reduced integration issues by 40% and accelerated release cycles. Developed robust unit and integration test suites using MSTest, ensuring code coverage and automating test workflows to prevent regressions during feature rollouts.

Summary

Optimized data retrieval and ensured integrity by leveraging advanced SQL queries on Airbnb's MySQL databases, enhancing the performance of analytics and reporting pipelines. Developed responsive and accessible user interfaces using HTML5, CSS3, and JavaScript, contributing to Airbnb's seamless digital booking and user experience across web platforms Collaborated with cross-functional teams including UX, product, and backend engineers through the full Software Development Life Cycle (SDLC), delivering a feature-rich application within a tight 4-month deadline. Maintained codebase integrity by implementing Git best practices, managing 40+ commits, conducting peer reviews, and driving efficient team collaboration across distributed engineering teams. Partnered with data scientists and business stakeholders to ensure technical solutions were aligned with Airbnb's strategic goals, improving application performance and user satisfaction. Automated functional and regression testing using Selenium, boosting test coverage and reliability of key Airbnb business applications while reducing manual QA efforts.