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About

I am a Software Engineer with a strong background in automation development, site reliability engineering (SRE), and security testing, specializing in cloud-native and scalable solutions. With hands-on experience in Python, Java, and JavaScript, I build robust systems that optimize performance, reliability, and security. In my previous roles at Dell Technologies and as a Graduate Research Assistant, I developed and deployed automation frameworks that significantly reduced manual efforts, improved test efficiency, and enhanced system resilience. My expertise spans across DevOps, cloud infrastructure, and security testing, leveraging tools like Terraform, Kubernetes, AWS (IAM, EC2, S3, Lambda), and Ansible to ensure high availability and fault tolerance. I have a deep understanding of software development in Agile environments, following best practices in CI/CD pipelines, test automation (Selenium, Cypress, Cucumber), and performance testing (JMeter, Postman). My experience with security tools like Splunk, Burp Suite, and WAF enables me to proactively identify vulnerabilities and enhance system defenses. Additionally, I am passionate about building scalable microservices, monitoring cloud infrastructure for SRE best practices, and integrating automation to streamline deployments and incident response. Whether it's ensuring high system reliability, enhancing test automation coverage, or securing cloud applications, I thrive on delivering resilient, high-quality software solutions.

Skills

Python
.NET
Javascript
Kotlin
HTML
React.js
Git
Entity Framework
Django
Jenkins
Selenium Webdriver
Cypress.io
Appium
Automation Development
SQL
Postman
Curl
Terraform
Kubernetes
HTTP/S
Docker
Agile Development
Deep Learning
Cypress.io
NoSQL
RestfulAPI
JMeter
BDD Framework
AWS EC2
CloudFormation
AWS IAM
AWS S3
Elastic BeanStalk
DyanmoDB
AWS SQS
API Gateway
Linux
Test Planning & Design
Penetration Testing
API Testing

Experience

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Graduate Teaching Assistant

University of Texas, Arlington
Jan 2025 - Present
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Graduate Research Assistant

University of Texas, Arlington
March 2024 - Dec 2024
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Software Engineer

Dell Technologies, Ireland
Nov 2019 - June 2023

Education

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University of Texas, Arlington

MS in Computer Science (Ongoing)
Aug 2023 - May 2025
University Of Limerickr

University of Limerick, Ireland

Master of Engineering (Thesis) in Information & Network Security
2018 - 2019
S.J.B Institute of Technology

SJB Institute of Technology, Bangalore

Bachelor of Engineering in Computer Science and Engineering
2013 - 2017

Portfolio

My Recent Work

Sentiment Analysis on the IMDB movie review dataset

This project evaluates LSTM, Bi-LSTM, and BERT architectures for NLP tasks, utilizing advanced preprocessing techniques like noise removal and stopword filtering to enhance data quality. BERT, fine-tuned with Hugging Face’s Trainer API using GPU acceleration and mixed-precision training, outperformed LSTM (89.15% accuracy) and Bi-LSTM (89.65%), achieving 93% validation accuracy with superior precision, recall, and F1 scores. The results confirm BERT’s dominance, showcasing its stability and effectiveness over traditional recurrent models.

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E-Commerece website - "Musiqi"

An online web music player application that seamlessly fetches and streams music using the Shazam API, providing users with an extensive and up-to-date music library.

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AI Image Generator

This is a react app that generates an image based on the user description entered in the generate text area. This application would demonstrate the integration of AI-powered services provided by OpenAI with modern web development frameworks like React, creating a seamless bridge between complex AI operations and user-friendly web interfaces.

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E-Commerece website - "Sneaky"

This project show cases the development of a web-based application named Sneaky. Sneaky is a website application that allows customers to buy vintage/rare sneakers. It also provides users the functionality to sell their sneakers by uploading them onto the website platform. Web based technologies like HTML, CSS and Javascript were implemented for the front-end design while Firebase was used as a database at the back-end

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Application of Deep Learning on Music Genre Classification (Master's Thesis)

This project provides the classification of music from 5 different genres based on the feature extracted during the pre-processing stage using the Python library Librosa. Deep Neural Networks is implemented to train the dataset using the TensorFlow library and using Keras built on top of TensorFlow.

Contact Me