Akshay Sharma

About

Software Engineer with over 2 years of experience in creating cutting-edge solutions for a variety of markets, such as fintech, data analysis, and healthcare. Proficient in a variety of technologies and programming languages, including Python, Java, AWS, JavaScript, React, Node, and Django. With expertise in both back-end and front-end development, I have consistently delivered scalable, top-tier systems known for their quality and reliability.

Experience

• In-House Software Intern · Jaypee Institute of Information Technology05-01-2020 – 06-30-2020

  • Designed a Price Monitoring Tool that retrieves the cheapest price of any product from the web • Used Selenium driver to search products on browser and scrap product details (price, availability, URL, estimated delivery date) • Compared prices across popular e-commerce websites like Amazon, Flipkart, Snapdeal, eBay, and Tata Cliq

• Google Summer of Code Developer · Python Software Foundation06-01-2021 – 08-31-2021

  • Introduced a Python library named ‘xtractmime’ implementing MIME (Multipurpose Internet Mail Extensions) sniffing standard • Implemented functionality to detect a MIME type group and added unit tests achieving 100% code coverage • Configured CI/CD pipeline for static checks (pytest, flake8, typing, black) using tox automation through GitHub Actions workflows • Rolled out version 0.1.0 to PyPI and integrated it into ‘Scrapy’ to help determine the MIME type of scraped content

• Software Engineering Intern · Honey Health Inc.05-18-2022 – 08-24-2022

  • Built backend services using AWS Serverless Application Model (SAM) for an iOS app helping diabetic patients • Programmed RESTful APIs as Lambda functions and enabled group chat feature using Chime SDK • Integrated with Dexcom G6 devices for continuous monitoring of user’s daily glucose data • Automated deployment using SAM Pipelines and coordinated with front-end engineers to develop an end-to-end mobile app

• Researcher | Machine Learning · United Nations Volunteer03-01-2023

  • Classified Landsat images using Machine Learning in Google Earth Engine to create a mangrove map for Qatar area • Evaluated 3 ML models (RF, SVM, CART) and differentiated final results with Global Mangrove Watch (GMW) • Conducted research to identify optimal spectral bands and vegetation indices using correlation matrices • Improved model accuracy by 6% through the identified combination of indices

Education

• University of FloridaGraduate Degree, Computer Science
• Jaypee Institute of Information TechnologyBachelor's Degree, Computer Science

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