Hi👋🏻 I'm Prateek!

and I'm

Software Engineer by Day, Innovator by Choice

Code by Day, Creativity by Night

About Me

My Introduction

I am a Software Engineer at Bharti Airtel, building scalable backend systems, full-stack applications, and enterprise-grade Python automation. I deliver RESTful APIs, database-driven platforms, and DevOps-enabled solutions that optimize NOC operations, reduce manual effort, and improve operational reliability.

2+ Years Work
Experience

My Resume

Skills

My Technical Level

Python

90%

C++

85%

JavaScript

75%

HTML

85%

CSS

85%

Django

95%

Django Rest FrameWork

95%

FastAPI

85%

React.js

80%

Tailwind CSS

80%

Docker

90%

Podman

90%

Git

85%

Networking

85%

Linux

80%

MySQL

90%

Oracle DB

85%

Rest APIs

90%

Qualification

My Journey

Education

Work

B.Tech in Computer Science and Engineering with Artificial Intelligence and Machine Learning

Maharshi Dayanand University, Rohtak, India

2020-2024

Higher Secondary in PCM with Computer Science (Python)

Blue Bells Public School, Gurugram, India

2019-2020

Secondary

Blue Bells Public School, Gurugram, India

2017-2018

Software Engineer

Bharti Airtel Ltd.

July 2024 – Present

Designed and deployed enterprise-scale Python automation scripts that streamlined NOC operations, reduced manual effort by 98%, eliminated errors, and delivered substantial cost and time savings.
Built backend systems and full-stack applications with Django REST Framework, MySQL, Oracle DB, React.js, Python.
Developed RESTful APIs, automated Excel-based reports, and implemented DevOps-enabled workflows to ensure SLA-compliant, real-time reporting and dashboarding.
Led cross-functional collaboration, gathered requirements, conducted risk assessments, and delivered audit-compliant automation frameworks minimizing service outage risks.

IT Intern

Hero MotoCorp Pvt Ltd

July 2023 - August 2023

Worked on IT project planning and execution, collaborating with team members to achieve project goals and timelines.
Worked closely with the senior team members of Data Scientists and Engineers on the development of the "Smart Eye" project, leveraging machine learning techniques for vision- based & object detection applications.

Machine Learning Intern

Feynn Labs

February 2023 - April 2023

Performed AI product/service prototyping for small businesses, leveraging machine learning algorithms to enhance operational efficiency and customer experience.
Conducted market research and data analysis to identify trends and patterns, enabling large- scale predictive market segmentation and Financial Modelling using machine learning techniques.

Portfolio

My Projects

Drowsiness Detection System

Computer Vision

Developed and implemented a real-time driver drowsiness detection system using machine learning techniques to enhance road safety and prevent accidents caused by drowsy driving.

Employed machine learning algorithms, such as Haar Cascade, dlib, OpenCV for Object Detection and Facial Landmark Detection, to extract relevant features from the driver's face and eyes for drowsiness detection and alert him with a beep sound plus capture its image and store it into MySQL database.

Tech Stack

View Code

WhatsApp Portfolio Website

Web Development

Designed and developed a web-based chat application with a user interface similar to WhatsApp, providing a platform to showcase a portfolio with all details in interactive demonstrations.

Features:- About, Skills, Resume, Education, Address, Contact, Projects.

Tech Stack

Visit Website View Code

CloudChaser Weather App

Web Development

The app is developed using React.js which fetches and displays up-to-date weather data using the OpenWeatherMap API, providing accurate real-time weather information for various locations around the world.

Shows the weather in Hindi as well as in English.

Tech Stack

Visit Website View Code

Real-time Attendence from CCTV

Computer Vision

Identifies the human face in real time from a CCTV camera with the help of computer Vision. If CCTV camera frame contains multiple faces,developed model have the capability to recognize all the faces in frame.

The library uses a pre-trained deep neural network model to identify and locate faces in images, and then compares these faces to a database in MySQL of known faces to perform recognition.