Kishore Batchala
Java Developer (Entry-level) • k*****************@gmail.com • +91*******503 • drivetube.ai/•••••
Professional Summary
Java Developer with 0 years of experience, strong fundamentals in Java, Spring Core, JDBC and Maven, and hands-on exposure to Python-based deep learning. Built Maven-based Spring Core applications implementing dependency injection, bean configuration and basic ORM with Hibernate during academic projects. Completed internships working on Vision Transformer image-classification models and end-to-end ML workflows using Python and Jupyter notebooks. Experienced designing data preprocessing, training pipelines, and simple web UIs backed by MySQL/JDBC for demo services. Seeking an entry-level Java Developer role where I can apply backend development skills, Spring ecosystem knowledge, and practical ML experience to deliver production-ready services and continue learning full-stack and cloud-native development.
Technical Skills
Programming Language: Java,Python,BASIC
Frontend Technologies: HTML,CSS
Backend Technologies: Spring,Hibernate,JDBC
Databases: MySQL
Version Control & Development Tools: GitHub,Eclipse,Jupyter,Maven
Work Experience
Coincent.ai
AI Intern
2024 – 2024
Worked at an AI-focused team on computer-vision model development for image classification and model training pipelines.
Tech Stack: Python, Jupyter, Vision Transformer
- Implemented a Vision Transformer (ViT) image-classification model in Python to prototype a computer-vision pipeline for classification tasks.
- Built a data-augmentation pipeline to synthesize varied image samples and improve model robustness during training.
- Designed the patch-embedding and positional-encoding modules for the ViT implementation to support fixed-size input patches.
- Optimized the training loop for faster iteration by applying batch handling and efficient data loading strategies in Jupyter notebooks.
- Tuned core hyperparameters (learning rate scheduling, batch size, optimizer selection) and validated models using standard evaluation metrics.
- Documented model architecture, training steps, and evaluation results to enable reproducible experiments and handover.
AI-ML Virtual Internship
AI-ML Intern (Virtual Program)
2024 – 2024
Completed a virtual internship program covering end-to-end machine learning workflows, model evaluation and deployment concepts.
Tech Stack: Python
- Completed hands-on modules on data preprocessing and feature engineering using Python to prepare datasets for model training.
- Implemented and evaluated neural network models for classification tasks and compared architectural choices across experiments.
- Designed experiments to compare optimization strategies and recorded results to guide model selection and tuning.
- Explored basic model deployment concepts and prepared model artifacts and inference notebooks for demonstration purposes.
- Applied validation and cross-validation practices to assess generalization and reduce overfitting in experimental models.
- Summarized learnings and produced technical reports detailing workflows, model choices, and next-step recommendations.
Projects
Spring Core Java Applications
Tools Used: Java, Spring Core, Dependency Injection, Bean Configuration, Autowiring, Maven, Hibernate Basics, JDBC, MySQL, HTML, CSS, Eclipse, GitHub
- Developed Maven-based Java applications using Spring Core to demonstrate dependency injection, bean configuration and autowiring patterns.
- Implemented multiple service implementations and interfaces to enforce loose coupling across components and simplify testing.
- Integrated JDBC with MySQL to persist domain data and wrote DAO layers demonstrating transaction handling and exception management.
- Applied basic Hibernate mappings for an ORM-based module to model entities and facilitate simple CRUD operations.
- Built a minimal web interface with HTML/CSS to exercise backend endpoints and verify service behavior during demos.
Human Stress Detection using ANN
Tools Used: Python, Artificial Neural Network, Data Preprocessing, Feature Engineering, Model Training, Jupyter, GitHub
- Developed an ANN-based classifier in Python to detect human stress levels using physiological and behavioral features.
- Performed data cleaning, normalization and feature-engineering to prepare time-series and tabular inputs for the network.
- Trained and validated model variants, monitored loss curves and adjusted architecture depth to improve validation performance.
- Evaluated model outcomes with confusion matrices and precision/recall metrics to quantify classifier behavior across classes.
- Packaged training notebooks and model artifacts in Jupyter for reproducibility and shared results via a GitHub repository.
Education
Raghu Institute of Technology
B.Tech in Computer Science and Engineering (AI/ML) • 2022 – 2026
Sri Prakash Junior College
Intermediate (MPC) • 2020 – 2022
Sri Prakash Vidya Niketan
SSC • 2020
Certifications
Introduction to Programming with Python — HarvardX
Principles of Generative AI — Infosys Springboard
AI Primer — Infosys Springboard
Responsive Web Design — freeCodeCamp
Programming in Java — NPTEL
Problem Solving in C — NPTEL
Achievements
- Participation in hackathons and coding contests: Competed in team hackathons and coding contests to strengthen teamwork, analytical thinking and problem solving.
- Multiple online certifications completed: Completed certifications in Java, Python, web development and AI/ML through recognized platforms (NPTEL, HarvardX, freeCodeCamp, Infosys Springboard).
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