• Developed cloud native solutions for aiding next-generation medical systems and applications.• Developed micro-service using Java with Spring Boot.• Contributed towards C# based medical systems application development.• Contributed towards prototyping and deploying services to cloud on top of Cloud-foundry's open-source Platform as a Service (PAAS).

Course: Networks and distributed systems

• Worked on AWS SageMaker team to develop a full-stack solution to give customers real-time metrics of code execution status in SageMaker studio notebook. Without metrics, customers found it difficult to understand code execution status.• Created a Jupyter server extension that calculated the CPU/Memory utilization metrics of the remote instance executing code.• Integrated the Jupyter server extension into a python-based tornado server which acts as the code execution manager running in each remote compute node. The integrated python module exposed the CPU/Memory metrics via REST APIs.• Extended a python based central proxy application by adding additional request handlers. The additional request handlers proxied the requests for metrics from the client side to the correct remote compute instance.• Created frontend using React and TypeScript that fetched and rendered metrics from the backend every 3 seconds. • Followed test driven development principles. Implemented tests for the frontend component using Jest and Enzyme. Did mock testing and snapshot testing for frontend. Did end-to-end integration tests and ensured the feature is deployed in production across multiple regions of AWS.

• Worked with Professors of National University of Singapore to learn about research applications of Big-Data and Machine Learning algorithms and ongoing research conducted at their research facility.• Worked on developing web-application which did sentiment analysis of feedback/comments received for a food chain. The application figured if the review is negative or not. The model was trained on reviews data extracted from yelp reviews.• The backend was developed using python-based flask webserver. • Implemented SVM based machine learning model in the backend to do the sentiment analysis on review gotten from the frontend client.