Computer Science Spring Boot

Vignesh Ambeeru

Tampa, FL, USA +1-656-***-**** ****************@*****.*** LinkedIn GitHub Portfolio EDUCATION

University Of South Florida Aug 2023 – May 2025

Master of Science in Computer Science, Tampa, FL, USA Malla Reddy College of Engineering & Technology Aug 2017 – Jul 2021 Bachelor of Technology in Computer Science, Hyderabad, India TECHNICAL SKILLS

Languages: Python, Java, JavaScript, TypeScript, C++ Frameworks/Libraries: Next.js, React.js, Redux, Node.js, Express.js, Spring Boot, Flask, FastAPI Cloud Platforms: AWS (Lambda, S3, ECS, EKS, EventBridge, Cognito), Azure (Functions, SignalR, Blob Storage) Databases/Messaging Systems: PostgreSQL, DynamoDB, MongoDB, MySQL, Redis, RabbitMQ, Kafka Tools: Docker, GitHub Actions, Swagger, Postman, Sonar, JIRA, JUnit, JMeter, Jenkins, CDK, CloudWatch, X-Ray, D3.js Architectures/Patterns: RESTful APIs, GraphQL, Microservices, Serverless, Event-Driven Systems, CI/CD, OAuth 2.0 EXPERIENCE

Application Development Associate Accenture, India Jul 2021 – Apr 2023

• Migrated 12+ services from a monolithic system to microservices using Spring Boot, Netflix Eureka, API Gateways and circuit breakers, eliminating single points of failure and increasing system resiliency.

• Architected an event-driven messaging system to process 5K+ transactions daily using Apache Kafka and RabbitMQ; inte- grated Redis caching layers to reduce backend API latency by 40% under peak production loads.

• Built a fully automated CI/CD pipeline with GitHub Actions, AWS CodePipeline, and Docker for deployments to AWS EKS, enabling zero-downtime blue-green deployments and reducing manual release efforts by 50%.

• Improved production reliability by integrating Zipkin for tracing, Zod + React Hook Form for validation, and 200+ tests via Cypress and Jest, boosting test coverage by 60% and reducing bugs. Associate Professional Software Engineer DXC Technology, India Jan 2021 – Jul 2021

• Engineered a React.js application implementing component memoization and virtual scrolling with react-window, achieving a 95ms render latency for data grids displaying over 10,000 records, improving user experience.

• Integrated real-time communication into a Node.js backend using WebSockets (Socket.IO), enabling live chat, presence tracking, and instant notifications while cutting polling overhead by 70%.

• Deployed a secure RBAC system with granular permissions and JWT-based authentication middleware, preventing unauthorized access and reducing breach attempts by 30% through enforced route-level controls.

• Tuned MongoDB for high-concurrency workloads (1K+ clients) through schema refactoring, compound indexing, and optimized queries, improving aggregation performance by 40%. PROJECTS

Uber Clone Project Developer — Full Stack Java GitHub Oct 2024 – Jan 2025

• Implemented a modular ride-matching platform using Spring Boot, PostgreSQL, and RESTful APIs, applying Strategy and Observer design patterns to reduce average ride allocation time by 50%.

• Ensured system robustness through 98% test coverage using JUnit, with layered architecture and clearly separated service- controller-repository modules to support maintainability and scalability. Music Genre Virtuoso Team Lead — Deep Learning GitHub Sep 2024 – Dec 2024

• Led the design of a genre classification system using a custom CNN architecture with Keras (VGG16), achieving 85% accuracy by applying advanced audio preprocessing techniques like MFCC and spectrogram analysis.

• Deployed the model into a real-time web app via Flask, integrating audio upload pipelines, prediction endpoints, and logging mechanisms to ensure a scalable user-facing ML service. E-Commerce Platform Developer — MERN Stack GitHub May 2023 – Jul 2023

• Designed a full-stack e-commerce system using MongoDB, Express.js, React.js, Node.js, integrating secure checkout flows with Stripe and PayPal, supporting 200+ daily orders with < 3s response time.

• Implemented global state management using Redux for cart and user sessions, and optimized component performance with lazy loading and caching strategies for cross-device consistency. Attendance Marking Based on Face Identification Team Lead — Python GitHub Jan 2021 – Mar 2021

• Developed a real-time face recognition attendance system using Python, OpenCV, and Tkinter, leveraging PCA for dimen- sionality reduction and LBPH for accurate face recognition, with automated CSV-based attendance logging. CERTIFICATIONS

Python for Everybody Specialization (Coursera), SQL (Codecademy), RESTful API with HTTP and JavaScript (Coursera), Oxford Achievers English Proficiency – Oxford University Press (2018) and CodePath Intermediate Technical Interview Prep (Advanced).

Contact this candidate