DEEPAK

Jhunjhunun

Summary

Driven AI intern specializing in deep learning and data analysis. Known for strong problem-solving skills and technical proficiency, successfully enhancing AI applications and optimizing workflows through innovative machine learning models.

Overview

year of professional experience

Certification

Work History

Artificial Intelligence Intern

ISTUDIO

WORK FROM HOME

08.2024 - Current

Implemented deep learning models to improve accuracy and efficiency of existing AI applications.
Created machine learning models to identify patterns in customer behaviors and preferences.
Identified opportunities for automation of manual tasks through the use of AI technologies.
Trained AI models with labeled datasets using various frameworks like TensorFlow, PyTorch.
Analyzed results from simulations and experiments to draw meaningful insights.
Tested AI products for accuracy and reliability prior to launch.
Tested, validated and reformulated models to foster accurate prediction of outcomes.

Education

MCA - DATA ANALYTICS

Chandigarh University

PUNJAB

01.2026

BCA - Web Programming

Rajasthan University

Jaipur

01.2023

Skills

Java
Data analysis
Python programming
SQL
HTML
NodeJs

CSS
Javascript
Machine learning
Deep learning
React

Additional Information

Fresher

Projects

Smart Resume Analyzer, 3 Days, Java, Spring Boot, Spring MVC, Thymeleaf, Apache PDFBox, Maven, Developed a full-stack web application to automate the initial resume screening process by calculating a percentage-based match score between a candidate's resume (PDF) and a given job description. Engineered a backend service using Java to parse unstructured text from job descriptions, filter common stop-words, and extract a unique set of key skills and requirements. Utilized the Apache PDFBox library for robust parsing of uploaded PDF resumes to extract the candidate's information into plain text for analysis. Designed and implemented a scoring algorithm that compares the extracted keywords against the resume content to quantify the candidate's alignment with the job profile. Created a dynamic user interface with Thymeleaf to display the analysis results, clearly visualizing the match score, along with lists of matched and missing keywords. Real-time Log Analytics Dashboard, 2 Days, Java, Spring Boot, Apache Kafka, Docker, Docker Compose, WebSockets, Maven, Designed and implemented a complete, event-driven system to process and visualize high-volume log data in real-time, simulating a production-level monitoring environment. Built a robust data pipeline using Apache Kafka to ingest continuous log streams from multiple producers. The entire Kafka and Zookeeper infrastructure was containerized using Docker Compose for a consistent and reproducible setup. Developed a Spring Boot consumer application that listens to the Kafka topic, performing real-time aggregation of log data (e.g., counting ERROR vs. INFO logs). Integrated WebSockets (STOMP) to broadcast the processed analytics from the backend to a dynamic frontend, creating a live dashboard that displays new log data instantly without requiring page refreshes.