Nageen Chand

Hi, my name is

Software developer

Experienced Java backend developer, specializing in crafting robust RESTful applications, microservice architecture based development and mastering SQL databases. I thrive in the realm of server-side development, where I turn ideas into functional realities. Let’s build the backbone of your digital ambitions.

Resume

About Me

I’m a consultant specializing in Java microservices at Deloitte USI. I hold Bachelor’s and Master’s degrees in computer science and bring expertise in backend development, Spring Boot, and cloud services.

My responsibilities include designing and deploying robust microservices, managing databases with SQL and MSSQL, and addressing priority issues. I also collaborate closely with clients to deliver innovative and efficient solutions.

Join me in developing scalable systems that power exceptional customer experiences.

Here are a few technologies I've been working with recently:

Java
REST APIs
Spring Boot
InterShop
MicroServices

Experience

Deloitte USI
WTC
Mirasys

Consultant - Java - Microservices - Deloitte USI

Dec 2024 - present

As a Java consultant, I specialize in developing microservices for our clients, focusing on RESTful APIs, Spring Boot, and cloud-native applications.

Associate Developer - Backend - WTC

Jul 2021 - Nov 2024

In my role at organization, I’ve improved our client’s ecommerce site by implementing an Express checkout feature, resulting in increase in conversion rates. I specialize in e-commerce website development using Intershop, focusing on payment gateway integrations, order management, and inventory control to deliver comprehensive solutions.
Collaboration is key to my work. I partner with project managers, designers, and QA testers to ensure high-quality solutions for our clients.
Additionally, I contribute to the development of internal tools and processes, including RESTful APIs and Java code, to support project delivery. My expertise in MVC architecture ensures our e-commerce solutions are both scalable and maintainable.

To stay at the industry forefront, I actively adapt to new technologies and methodologies like Agile development, providing our clients with cutting-edge solutions.

Computer Vision Intern - Mirasys

May 2020 - Jul 2020

As a Computer Vision/Machine Learning intern, I gained practical experience working on real-world applications of object detection using Deep Learning. This hands-on internship provided valuable insights into applying cutting-edge technology to solve real-world problems.

Education

2016 - 2021

Master of Technology in Computer Science

National Institute of Technology, Hamipur

GPA: 9.22 out of 10

I have completed my Masters of Technology in Computer Science and Engineering from NITH, My Dissertation for the course was on

Data Fusion for IoT based Devices using Dempster-Shafer Evidence Theory : Focused on optimizing IoT data management by applying Dempster-Shafer Theory for efficient data fusion. Aimed to reduce data transmission and enhance privacy in heterogeneous sensor networks.

Research Project was on

Abnormal Vehicle Detection System : Developed real-time abnormal vehicle detection system using YOLOv3, optical flow analysis, and SVM classifier, achieving 93.57% accuracy. Leveraged deep learning for object detection and tracking, optical flow for motion analysis, and SVM for classification. Trained and validated model on large-scale BDD100K open dataset containing over 100K videos

Key takeaways from the course:

Proficiency in Python, Java, and C++
Specialization in data science, AI, and computer vision
Research experience in emerging technologies
Collaboration with professors and peers

Extracurricular Activities

Volunteering experience and active member of GLUG-NITH during graduation

2016 - 2021

Bachelor of Technology in Computer Science

National Institute of Technology, Hamipur

GPA: 8.2 out of 10

During my B.Tech. program, I immersed myself in computer science, programming, and software development. I tackled a notable project in my final year, which focused on:

Enhancing Pedestrian Movement Detection in Driving Assistant Systems : In this project, we addressed the critical challenge of accurately detecting the direction of pedestrian movement in driving assistance systems. Our goal was to develop software that would contribute to a safer driving experience for both pedestrians and drivers in various scenarios, including zebra crossings, public demonstrations, parking areas, and shopping centers.

Key components of the project included:

Utilizing the YOLO (You Only Look Once) algorithm for pedestrian detection and localization.
Calculating optical flow vectors for each detected pedestrian using dense optical flow analysis.
Inputting segmented pedestrian data into a neural network to predict movement direction.

Extracurricular Activities

Volunteering experience and active member of GLUG-NITH during graduation

Class XII

Vardhman Mahavir Pubic School, Sunder Nagar

Aggregate: 88.8 %

Course includes Physics, Chemistry, Mathematics, English (language) and Computer Science and its Applications.

Class X

Vardhman Mahavir Pubic School, Sunder Nagar

Aggregate: 89.57 %

Projects

Cloudflare Serverless Cloudflare KV

Url Shortner

Simple Serverless URL Shortner designed using Cloudflare workers and KV developed using typescript and have features like creating short url, redirecting to target url using short url, listing all created short urls, deleting short urls with credentials only. Please checkout this at

Try it out!!

Java Spring Boot MongoDB Javascript

JumboDeals

Designed and built responsive web application with Spring Boot and Thymeleaf templating to aggregate and display product listings from e-commerce sites. Also implemented scheduler-driven automation for periodic data refresh and update, ensuring seamless user experience across devices

Demo

Python Research Project Object Detection

Abnormal Vehicle Detection System

Developed real-time abnormal vehicle detection system using YOLOv3, optical flow analysis, and SVM classifier, achieving 93.57% accuracy. Leveraged deep learning for object detection and tracking, optical flow for motion analysis, and SVM for classification. Trained and validated model on large-scale BDD100K open dataset containing over 100K videos

Github Source