• Led a team of 5 to gather requirements and develop employee hiring portal using JAVA Spring boot and React.js.• Implemented a CI/CD pipeline using GitHub Actions to automate the deployment of Docker images to AWS EC2, optimizing the Docker image size and reducing build time by 35%.• Integrated ChatGPT APIs to automatically extract and upload text details from document images, reducing onboarding time by 50%.

• Completed back-end of ticketing portal for the Information security of CSULB in Node.js and AWS S3 as storage.• Transformed SQL queries for 100K+ tickets by restructuring indexes and joins, improving search speed by 25%.• Leveraged cron jobs to automate email notifications for unresolved tickets, reducing the resolving time by 50%.• Designed an ETL pipeline with AWS Lambda to automatically extract data from S3, send email attachments, and feed it into Power BI for real-time analysis with daily refresh.

• As a Teaching Associate for CECS 174 (Introduction to Programming), I played a pivotal role in guiding students through the fundamentals of Python programming and key concepts in object-oriented programming. • Crafting assignments and examination materials, I ensured students received a comprehensive understanding of the subject matter. Additionally, I facilitated their familiarity with essential coding tools such as Visual Studio Code and GitHub, empowering them with practical skills for real-world application.

• Developed the world's first Intelligent Virtual Assistant (IVA) for freight and logistics, utilizing Dialogflow as the natural language processing engine, Python-Django as the middleware, and Postgres and MongoDB as the databases. Leveraged the Google Cloud Platform for computing. Deployed the IVA on WhatsApp and Google Assistant as integrated channels.• Implemented the Elastic stack to store logs and unidentified phrases, enabling seamless integration with Dialogflow and automating model training processes.• Stored data in redis for specific modules to process real time data like refering to previous conversations to significantly reduce response time by 60%.

• Worked on QAANAT, bank parser, an intelligent bank statement parserthat provides decisions to bank/financial institutions for providing loans.• Used regular expression to clean data and created algorithms to parse bank statements of different banks present in India,45+ banks with each bank having 3-5 different bank statement patterns.• Extracted useful information for analysis using SpaCy, advanced NLP model for label extraction using an automated scrapper developed according to the needs.