Software Engineer

HEMANVITHA MYLAPALLI

Jersey City, NJ ***** ***********@*****.*** 551-***-**** LinkedIn GitHub

EDUCATION

Pace University, New York, NY - Master of Science (MS) in Computer Science - GPA: 3.88 Sept 2022 – May 2024

Andhra University, India - Bachelor of Technology (B.Tech) in Computer Science - GPA: 7.67 Aug 2016 – May 2020

TECHNICAL SKILLS

Programming Languages: C, C++, Python, Java Web Development: HTML, CSS, JavaScript, React, Ionic Database Management: MySQL, Oracle Software Methodologies: Agile, Scrum, Retrospective, User stories, Personas, XP Programming Tools: Jira, GitHub, Figma

EXPERIENCE

Software Analyst Intern S Linx LLC – Leander, Texas August 2024 – September 2024

Collaborated with senior developers to analyze system requirements and translate them into technical specifications, improving the clarity and quality of requirements, which reduced ambiguity and ensured smoother project execution.

Led code reviews, recommending enhancements in logic and efficiency, which elevated code quality by 20% and reduced future maintenance efforts.

Executed unit testing by designing and implementing test cases for new Java code, detecting 15 bugs and strengthening the reliability of the software product.

Authored comprehensive technical documentation, which accelerated onboarding for new interns and developers, boosting productivity and adaptation to the codebase.

Software Engineer (Capstone Project) Pace University – New York City January 2024 – May 2024

Led an 8-member team using Agile Scrum, completing the project ahead of schedule with 100% task completion and boosting productivity by 20%.

Built an MVP using C and Python on Linux, improving data handling efficiency by 25% and scaling for large datasets.

Developed an image recognition system with CNNs, boosting data retrieval speed by 40% and cutting manual effort by 50%.

Led a recipe-sharing platform project using Agile and Jira, reducing development time by 15%.

Software Engineer MGS Technologies – Hyderabad, India June 2020 – August 2022

Optimized the 'Chain Creator' platform by refactoring C code, improving load times by 30% and enhancing scalability for large datasets, increasing user retention by 15%.

Engineered and launched a React dashboard for real-time site oversight, enhancing user experience by 20% and streamlining cross-team collaboration for quicker issue resolution.

Implemented data structures to optimize database queries, reducing response times by 25% and improving overall system performance.

PUBLICATIONS

Automatic Vehicle Speed Control 2020

Published a research paper on an IoT-based vehicle speed control system, designed to regulate speed in critical areas like hospitals and schools.

Designed and implemented a system using zone-specific encoders and in-car decoders, with a microcontroller automatically regulating vehicle speed upon entering designated areas.

ACADEMIC PROJECTS

NeighborSnoop September 2023 – December 2023

Crafted an intuitive Figma prototype for NeighborSnoop, optimizing the interface for performance and scalability, delivering real-time insights on neighborhood crimes.

Built and refined a user-friendly interface through Agile methodology, streamlining the tracking of local incidents and alerts, and elevating user engagement by 20%.

Incorporated visual elements for ease of navigation, enabling users to quickly access crime data, reducing search time by 30%, and enhancing user experience.

PaceFinity January 2023 – May 2023

Architected and built a fiscal management app using C and Python, empowering students to track expenses and manage scholarships, boosting financial literacy by 15%.

Integrated aid calculators and scholarship search tools, enhancing performance and scalability, and reducing financial stress for users by 25%.

Collaborated with a team of 3 developers using Agile methodology, conducting research and testing, leading to a 30% increase in user engagement and usability improvements.

Auto Moderator January 2020 – May 2020

Deployed encoders in specific zones that emitted unique codes, detected by vehicle decoders, triggering the microcontroller to automatically reduce speed.

Reduced speeding incidents by 30%, with the project receiving praise for the potential to improve urban traffic safety and management.

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