Jan 2023 – Jul 2023 · 6 mos · Bengaluru South, Karnataka, India · Remote

• ● Worked with the Backend team on the core product.
• ● Wrote AWS lambda functions to fetch event data from API and used Docker with AWS RIE to run lambda functions locally (serverless).
• ● Wrote an API and PostgreSQL queries that fetch data for different graphs representing client statistics. This API was integrated into the main application (production).
• ● Coded parts of the “Coupon Overview” feature of the application entirely from scratch.

Jul 2022 – Dec 2022 · 5 mos · Remote

• Selected amongst the top 200 out of 10000 applications.
• ● Underwent the “Machine Learning” and “Deep Learning” Specializations sponsored by Google India.
• ● Participated in Kaggle competitions on problem statements created by the Google team exclusive to the Bootcamp candidates.

Jul 2022 – Aug 2022 · 1 mo · Singapore · Remote

• ● Modified and developed the main backend application codebase consisting of 200+ lines of code for local deployment of the platform backend.
• ● Automated the process of database dump restore and setting up of local server for application deployment.

May 2021 – Dec 2021 · 7 mos · Hyderabad, Telangana, India · Remote

• ● Used Machine Learning algorithms such as Linear Regression and XGBoost to datasets of the spectral response acceleration parameters of various US regions like Alaska, Hawaii, Puerto Rico, Conterminous US to calculate the correlation coefficients among the Ss and S1 seismic hazard parameters based on the accuracy score of each of these models.
• ● The data was taken from the respective csv files and drawn into suitable dataframes so as to analyze the variation of Ss and S1parameters. Some of the libraries used in the process were Pandas, SciPy, Seaborn, Matplotlib, Numpy and Scikit-learn (sklearn). The trends were also represented diagrammatically through heatmaps and pairplots for correlation.