Trayi Chaganti

Full-Stack Developer

Java • Spring Boot • Angular/Vue • AWS • REST/GraphQL • Jenkins CI/CD

Full-Stack Java Engineer (3+ years) building microservices & event-driven systems with Kafka, automating CI/CD, and deploying containerized services on AWS with Docker/Kubernetes — focused on security, scalability, and observability.

Microservices Kafka AWS ECS Terraform Angular
Working

About Me

I specialize in secure, scalable microservices with Spring Boot and performant front-ends in Angular/Vue. I love designing event-driven architectures, automating CI/CD, and shipping cloud-native apps on AWS.

Recent highlights: Kafka-backed workflows for high-volume claims, Terraform IaC for standardized environments, and observability with CloudWatch/Prometheus. Clean code, great UX, and reliability are my priorities.

Security-first Scalable Systems Observability

Skills

Technical

  • Java • Spring Boot • JPA
  • Angular / Vue • TypeScript
  • Microservices • REST/GraphQL
  • Apache Kafka
  • AWS (EC2, ECS, S3, VPC, Lambda)
  • SQL • PostgreSQL • MySQL • DynamoDB
  • Docker • Kubernetes
  • JUnit • Swagger/OpenAPI

Tools & Strengths

  • Jenkins • GitHub Actions • Maven • Git
  • IntelliJ IDEA • VS Code • Android Studio
  • Terraform • CloudWatch • Prometheus
  • Clean Code • Testing • Mentoring
CI/CD IaC Security

Work Experience

Software Development Engineer

Sep 2024 – May 2025 • Texas, USA

CVS Health

  • Built Java Spring Boot & Node.js microservices exposing versioned REST/GraphQL APIs.
  • Implemented Kafka workflows for async processing & real-time status propagation.
  • Secured services with OAuth2/JWT, enforced mTLS, and standardized CSRF/XSS protections.
  • Containerized with Docker & deployed to AWS ECS; instrumented via CloudWatch & Prometheus.
  • Terraform IaC (VPCs, subnets, security groups) to standardize environments.
  • Angular 16 UI with NgRx + D3.js; modernized legacy modules with Vue.js.
  • End-to-end CI/CD in Jenkins automating build, test, and deploy.

Associate Software Developer

Jan 2023 – Jun 2023 • Hyderabad, India

CognitiveBotics Technologies

  • Developed Spring Boot microservices with REST APIs & Swagger/OpenAPI.
  • Optimized persistence with Spring Data JPA.
  • Angular 12 components using RxJS for reactive interactions and state.
  • Improved CI/CD with Jenkins & GitHub Actions; added unit/integration & Cypress e2e tests.

Java Developer

Jun 2020 – Dec 2022 • Hyderabad, India

Honeywell

  • Built Core Java services consuming REST APIs on AWS EC2.
  • Modeled domain relationships with Hibernate for performance & maintainability.
  • Led AngularJS → Angular 8 migration with hybrid strategy & reusable directives.
  • Improved UI quality with Karma/Grunt tests; deployed via Elastic Beanstalk with least-privilege IAM.

Education

M.S. Computer Science

2025

University of Central Missouri — GPA: 3.71

B.S. Computer Science Eng.

2022

Blekinge Institute of Technology, Sweden — GPA: 3.4

B.Tech Computer Science Eng.

2021

JNTU Kakinada, India — GPA: 3.4

Featured Projects

Coddle App

Coddle (Pet Care App)

Android mobile app for pet wellness with real-time notifications, grooming reminders, daily step tracking, and GPS-based activity monitoring. Integrated Google Maps API for precise location features.

Android Studio Java Google Maps API
CI/CD Jenkins Pipeline

CI Environment with Jenkins & GitHub Actions

Built a CI setup that responds to every push, builds the system, and runs unit, integration, and system tests through automation for faster, reliable delivery.

Jenkins GitHub Actions JUnit 5 Maven
Wine Quality Prediction ML

Wine Quality Prediction (ML)

Logistic regression model to predict wine quality from features like pH, acidity, and alcohol concentration. Classifies wines as “Good” or “Not Good” based on probability thresholds.

Python Scikit-learn Pandas
Bachelor’s Thesis on Train Ethernet Anomaly Detection

Bachelor’s Thesis: Comparison of Machine Learning Algorithms for Anomaly Detection in Train’s Real-Time Ethernet

Research on supervised ML for anomaly detection in TCMS networks. Compared Decision Tree, Random Forest, KNN, SVM, and Naive Bayes with PCA/feature selection; the Decision Tree achieved 98.89% accuracy in 0.19s.

Machine Learning Intrusion Detection Research

Get In Touch

Open to new opportunities, freelance projects, or a quick chat about building resilient cloud systems.

+1 (224) 566-4687
Kansas, USA