Dipesh Kumar

Graduate Intern

Bangalore, IndiaSoftware Engineering
1roles
45skills
1education
3credentials

About

Software Engineer focused on Java backend development, Spring Boot microservices, REST APIs, database systems, testing automation, and CI/CD.

Experience

Graduate Intern

Zeta

Jan 2026 – Jun 2026 · Bangalore, India

Worked with Spring Boot microservices and REST APIs to validate enterprise workflows and distributed service interactions. Migrated a legacy validation framework to REST Assured and Cucumber, reducing manual validation effort by 70%. Automated end-to-end workflow coverage across 11 execution stages and designed coverage for 12+ connector types. Integrated automated execution into Jenkins CI/CD pipelines and participated in sprint planning, code reviews, defect triage, and release validation.

Education

Jain (Deemed-to-be-University)

Bachelor of Technology, Computer Science and Engineering

Aug 2022 – Aug 2026

CGPA: 9.0 / 10

Skills

JavaPythonJavaScriptSpring BootSpring FrameworkSpring MVCMicroservicesREST APIsSpring Data JPAHibernateReactMySQLPostgreSQLMongoDBDockerJenkinsGitGitHubKubernetesLinuxCI/CDPostmanJUnit 5REST AssuredCucumberTestNGObject-Oriented ProgrammingCollections FrameworkException HandlingMultithreadingDesign PatternsDistributed SystemsAgile ScrumSDLCComputer NetworksOperating SystemsMavenSpring SecurityFlaskMachine LearningNLPTensorFlowPyTorchJWT authenticationRole-based access control

Projects

Enterprise Employee & Payroll Management System

Built a payroll and finance management system using Java, Spring Boot, REST APIs, Spring Data JPA, Hibernate, and MySQL, with employee, payroll, attendance, and authentication modules. Implemented JWT authentication, role-based access control, API Gateway, service discovery, Docker, and CI/CD-oriented architecture.

AI-Powered Decision Support System for Smart Farming

Developed an AI-powered agricultural decision support platform with crop recommendation, plant disease detection, weather forecasting, market analytics, and a multilingual NLP chatbot. Built backend APIs and machine learning inference pipelines using Python, Flask, TensorFlow, and PyTorch. The published system achieved 94.7% chatbot accuracy and 96.1% disease detection accuracy.