Computer Science Software Engineer

Sagar Krishna Kashyap

*** ****** ****** **** **, Atlanta, Georgia 30324

404-***-**** # *****************@*****.*** ï linkedin.com/in/sagarkk § github.com/sagar7krishna Education

Georgia State University Aug 2022 – May 2024

Master’s of Science in Computer Science (CGPA 3.7) Atlanta, Georgia 75% Merit Based Academic Scholarship

Assisted professors in evaluating assessments (quizzes, midterms, assignments, final exams) for Principles of Computer Science, supporting 200 students.

Automated late submission grading through a Python GUI, reducing manual calculation time by 75%. SRM Institute of Science and Technology July 2016 – May 2020 Bachelor of Science in Computer Science and Engineering (CGPA 7.65) Chennai, Tamil Nadu Skills

Languages: Python, Java, C, HTML/CSS, SQL, JavaScript, PHP, Arduino Sketch Databases: MySQL, MongoDB

Developer Tools: VS Code, IntelliJ IDEA, Android Studio Technologies/Frameworks: Linux, React, GitHub, Firebase, Cloud Computing, Selenium Soft Skills: Communication Skills, Problem-Solving, Teamwork and Collaboration, Time Management, Attention to Detail, Adaptability, Leadership, Customer Focus

Experience

Cognizant Technology Solutions July 2020 – June 2022 Junior Software Engineer Hyd, India

• Distinguished as a specialist, responsible for managing approximately 2,160 pivotal cases. Tasks included verifying developer legitimacy, conducting policy compliance testing, and facilitating valuable insights exchange between the appeals team and Google Full-Time Employees (FTEs), all achieved with an impressive 98% accuracy rate.

• Elevated trainees’ work proficiency and policy expertise through personalized communication and teaching techniques. Successfully trained and prepared a team of 10 new members for project inclusion, ensuring their readiness to work within an impressive two-week timeframe. This achievement contributed to the project’s efficiency and seamless integration of new team members.

• Developed automated UI tests leveraging Selenium WebDriver framework to accelerate testing, bolster quality, and achieve over 90% coverage for key user journeys.

Projects

Movie Review Application MongoDB, Java, Spring Boot, React January 2024

• Designed and implemented a modern movie review application leveraging MongoDB for efficient data storage, Java for robust server-side logic using the Spring Boot framework, and React for a dynamic and responsive user interface on the client side.

• Established a loosely coupled architecture, enabling independent evolution of client and server components. This design choice enhances scalability and maintainability, allowing seamless updates to the user interface (React) and server-side logic (Java, Spring Boot) without disrupting each other.

• Demonstrated expertise in full-stack development by seamlessly integrating technologies such as MongoDB for data persistence, Java and Spring Boot for backend services, and React for building a sophisticated and user-friendly front-end. This project showcases proficiency in handling diverse technologies and implementing a well-organized separation of concerns for optimal software development. Smart Parking Lot using Digital Twin Arduino, AWS services (IoT Core, IoT Sitewise, TwinMaker) October 2023

• Developed an IoT-based smart parking system using ultrasonic sensors, Arduino, and AWS services (IoT Core, IoT Sitewise, TwinMaker).

• Built a digital twin of a 3-space parking lot using Blender, accurately mirroring real-world layout.

• Integrated real-time occupancy data from 3 ultrasonic sensors via AWS IoT Core and Sitewise.

• Reduced parking search time and optimized lot utilization through sensor-driven occupancy monitoring. Comparative Analysis of Machine Learning Algorithms on Landslide Python, NumPy, Scikit April 2020

• Designed and executed a project utilizing landslide datasets in conjunction with associated triggers.

• Developed Decision Tree (DT), Support Vector Machines, Logistic Regression, and Random Forest Classifier (RF) models as part of the project.

• Conducted comprehensive experimental evaluations to identify the most suitable algorithm for the project’s objectives. Determined that the Random Forest Classifier (RF) outperformed all other algorithms, achieving an outstanding accuracy rate exceeding 90% whereas the DT model exhibited the lowest accuracy, approximately 80% .

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