Vismaya Anand Bolbandi
Riverside,CA
Summary
Hi, I'm Vismaya Anand! I have about 2.9 years of experience as a PKI-integrated Full Stack Developer and am currently pursuing my Master's in Computer Science with a specialization in Artificial Intelligence at the University of California, Riverside (UCR).
I'm open to Software Development Engineer (SDE) Intern and Machine Learning Engineer Intern roles, where I can apply my skills to solve real-world challenges. I have a strong background in building secure web and mobile applications and love working in collaborative environments. With a results-driven mindset and a positive attitude, I thrive in team settings and enjoy tackling complex problems.
Excited to connect and explore new opportunities!
Overview
3
3
years of professional experience
4
4
Certification
Work History
Associate Software Engineer
DigiCert
Bangalore, Karnataka
07.2022 - 09.2024
Full Stack Developer | Java, PHP, SQL, GraphQL, VueJS, Kafka, Linux, Docker, Cryptography
● Worked on Core Certificate Functionalities, Security Filters, Financial and Order Service Controllers
● Contributed in developing the newly launched Subscription Model microservices by working extensively on
GraphQL APIs in Java
● Developed REST APIs, added new features and enhancements to the existing product according to
their requirements using PHP and SQL as Database Language.
● UI - Designed Side-navigation bar with active menu and optimized existing UI features for the better.
Associate Software Engineer Intern
DigiCert
Bangalore, Karnataka
01.2022 - 07.2022
I worked on automating the existing user interface and building a testing tool for the quality assurance team using Node.js, Java, Selenium, and Cypress tools.
Education
Master of Science - Computer Science
University of California Riverside
Riverside, CA 09.2024 - Current
Bachelor of Technology - Computer Science
PES University
Bangalore, India 08.2018 - 06.2022
Skills
● Languages: Python, C, C, Java, PHP, JavaScript, HTML, CSS
● Frameworks - LLVM Framework, Intermediate Representation, FunctionPass, PySpark, Hadoop HDFS, NodeJS, VueJS, ReactJS, Geopandas, Plotly, PyTorch
● Query Language: GraphQL
● Database: MySQL, Firebase, Redis
● Blockchain: NodeJS, VueJS, ReactJS
● Developer Tools: Linux, Docker, PhpStorm, IntelliJ, Postman, Visual Studio, Hadoop, Numpy, Pandas, PyTorch, Kafka, Android Studio, GitHub
● Others: Compiler Design, Data Structures & Algorithms, OOPS, Docker, Complexity Analysis, Low Level Design, Test Driven Development, Software Development Life Cycle, CI/CD
Certification
● Data Science Foundations: Fundamentals – By Barton Poulson, LinkedIn Learning Dec 2023
● Career Essentials in Generative AI by Microsoft and LinkedIn Jan 2023
● Build Blockchain & Cryptocurrency | Full Stack Edition - By David Joseph Katz, Udemy July 2021
● Android App Development – By PESU/IO June 2020
Projects
● Compiler Optimization: Implemented custom LLVM compiler passes, including Reaching Definition Analysis for data-flow analysis and Common Subexpression Elimination for runtime optimization. Analyzed and transformed LLVM IR by traversing functions, basic blocks, and instructions. Automated input generation and testing using shell scripts integrated with LLVM’s opt tool.
● Retail Analytics with Big Data & ML: Built scalable analytics framework using PySpark, Hadoop HDFS, & advanced ML models. Applied K-Means Clustering for customer segmentation, Random Forest Regression for CLV estimation, Decision Trees for sales forecasting & conducted geospatial sales analysis using Geopandas to uncover regional trends, optimizing customer retention, inventory management, & marketing strategies.
● Pac-Man AI Search Algorithms: Implemented AI search algorithms (DFS, BFS, UCS, A*, & Greedy Search) in a Pac-Man environment to optimize complex maze navigation & food collection.
● Plastic Bank: A Blockchain based web-app for users to exchange recyclable plastics in exchange of cryptocurrencies.
● Passenger Security Using Facial Emotion Recognition: Built a real-time facial emotion recognition system using Deep CNN to enhance passenger security by detecting potential threats. Implemented using Python, OpenCV, and Keras, with an optimized pipeline for efficient video stream processing.
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