Working on a Retrieval-Augmented Generation (RAG) application, implementing end-to-end data pipeline management for ingestion, PII removal, indexing, and real-time response synthesis across multiple data sources.

• Worked on finance-related data processing and visualization for the Enterprise Application Services division

• Utilized Hibernate ORM in a Java backend to relay finance data from a MySQL database to a frontend Angular application

• Created interactive charts and tables while managing state through the use of Angular observables and subjects

• Weekly meetings with finance and dev leadership to discuss how to improve efficiencies with front-end features

• Worked on a CEC-funded project to build software to aid with the annotation of 1000+ deep-sea videos

• Improved video processing speed, originally done manually, by a factor of 10

• Used NodeJS, React, and AWS Elastic Beanstalk to develop a website for users to annotate videos to create training data

• Leveraged user-generated annotations to train a neural network classifier to automatically annotate videos

• Utilized machine learning and computer vision libraries such as TensorFlow and OpenCV to develop ML models

• Led frontend dev on a web app for finding and comparing 1000+ past projects to improve efficiency and record keeping

• Leveraged NodeJS, Docker, and the Elastic (ELK) stack for unifying a heterogeneous set of back-end data sources

• Used React to create a configurable UI for displaying JPL’s history of software for autonomy and AI related projects