- Translated system requirements into the design and development of customized systems in an agile environment.- Crafted Junit, Cypress, and Zephyr tests to confirm functionality and improve code coverage.- Assisted migrating a legacy application from AngularJS, Java 8, and junit4 to Angular 15, Java 17, and junit5.- Participated in Agile ceremonies such as sprint reviews, planning, retros, and daily stand-ups.

- Translated system requirements into the design and development of customized systems in an agile environment.- Crafted Swagger definitions for AWS Lambdas which leveraged Java and Quarkus for their construction.- Conducted rigorous testing of API endpoints employing Postman, cross-referencing the Swagger definitions that were originally designed for the on-premises iteration of the APIs, thus ensuring adherence to the expected behavior.- Contributed to the development of AWS Lambda APIs.- Demonstrated proficiency in composing CloudFormation templates, utilizing them to facilitate the provisioning of DynamoDB tables.

- Skillfully overseeing cash office management and ensuring precise balance.- Operating the customer service desk, addressing customer concerns, processing returns, and facilitating exchanges.- Effectively coordinating front-of-house operations for seamless functionality.- Offering valuable support to various departments as required.

- Establish project prerequisites and facilitate their conversion into functional components.- Orchestrated regular meetings, either on a weekly or bi-weekly basis, involving the project's sponsor, the Navy, and the subject-matter expert (a professor).- Utilized JavaScript, Python, and Flask to facilitate the seamless transference of environmental data from the Navy's training simulator to the Python-based training agent, an artificial intelligence entity. Furthermore, this approach facilitated the transmission of the agent's elected actions back into the training environment.Abstract: The United States Navy routinely conducts missions involving the detection and tracking of submarines by employing Sonobuoys. These Sonobuoys are deployed in the water to utilize underwater sounds for determining the approximate position of submarines. Nevertheless, the complexity of this mission is exacerbated by factors such as human error and the evasive maneuvers executed by submarines. Our initiative employs a Reinforcement Learning agent, implementing Deep Q-Learning, to take control of an established Navy simulator which is now onboard the aircraft. This system offers recommendations regarding the optimal timing and locations for Sonobuoy deployment. The broader community benefit of this endeavor is to enhance the capacity to detect and track submarines with potentially hostile intentions more effectively.