Hi there! My name is Ashish and I'm a Software Engineer at Fidelity . I help organizations to develop and maintain their code-base, products, and services. I have a Master of Science degree in Computer Science from Pennsylvania State University. I enjoy cycling and listening to tech podcasts in my free time.
I am a member of the Asset Management team, responsible for design and development of modern cloud-based web applications with a focus on user experience, scalability, high availability and low latency. I spend time designing and developing modern web apps using Angular SPAs & Spring Boot micro-services following OpenAPI specification.
Lead an agile team of 4 members developing an enterprise-class Cash Management Solution for a leading international financial institution
Performed data analysis and visualization on Amazon Fresh’s multivariate datasets using Python, Pandas, Matplotlib, IPython, and Jupyter. Developed a new recommendation strategy geared towards Amazon Fresh’s grocery business with the target to reduce the current cart abandonment rate
Developed and deployed several modules of a highly scalable enterprise-class Internet Banking web application for financial institutions using Java, Spring, Hibernate, JSP, REST APIs, JavaScript, and Finacle, following Agile methodology, with a focus on user experience (UI/UX), low latency, and high throughput. Upgraded native applications built for different mobile platforms to hybrid mobile applications using Apache Cordova, Java, HTML and CSS, reducing the overall development and deployment time by 30%. Developed native Android applications for financial institutions using Java, Finacle Mobile Banking Solution & IBM MobileFirst/worklight.
A highly scalable and distributed web application that provides fast object detection in video files and executes within a JavaScript-enabled browser. Developed using Node.js, Express.js, SQLite, and the MobileNet object detection model from TensorFlow.js library.
A highly scalable micro-blogging native application for Android to be used by the Penn State community. Developed using Java 8, Android SDK, JavaScript, XML, Google Firebase and Google Firestore.
Used k-means algorithm to cluster demographically similar users to provide a heuristic to resolve the cold-start problem in a Collaborative Filtering based hybrid recommendation system. Developed using Python, Pandas, Scikit-learn, Keras and Matplotlib.
