I am a software engineer with strong experience in backend development, data pipelines, and cloud-deployed systems. I work primarily with Python and Java, building scalable APIs and services using Flask and Spring Boot, and integrating them with modern front-end frameworks such as React. I have deployed and operated applications on AWS and GCP using Docker and Kubernetes, and have designed data solutions using PostgreSQL, MySQL, MongoDB, BigQuery, and Elasticsearch. Alongside backend engineering, I have delivered analytics and reporting solutions using SQL, Tableau, and Power BI to support data-driven decision-making.
I work effectively in Agile environments, contributing to sprint planning, implementation, and delivery in Scrum-based teams. Through my role as a Course Assistant and leadership responsibilities on academic and professional projects, I have developed strong communication and collaboration skills while guiding teams through complex technical requirements. I am analytical, adaptable, and execution-focused, with a proven ability to break down ambiguous problems and deliver reliable, scalable solutions in both independent and cross-functional settings.
Rochester Institute of Technology | Aug 2023 - Dec 2025
GPA: 3.71/4.0
I have completed a Master of Science in Computer Software Engineering at Rochester Institute of Technology (GPA: 3.71), with a focus on backend systems, cloud-native development, and data-driven software engineering. Through rigorous coursework and hands-on roles as a Course Assistant and Dashboard Developer, I designed and optimized scalable APIs, analytics pipelines, and distributed systems using Python, Java, Spring Boot, SQL, and cloud platforms such as AWS and GCP. This experience strengthened my ability to build reliable, high-performance software systems and apply data engineering and analytical techniques to real-world problems at scale.
View CertificateLJ Institute of Technology and Engineering | Aug 2019 - May 2023
CGPA: 8.52/10
I completed my Bachelor of Engineering in Electronics and Communication at Gujarat Technological University, gaining a robust understanding of core technical concepts and hands-on engineering skills. This program equipped me with critical analytical abilities and a deep appreciation for the interplay between hardware and software, setting the stage for my subsequent pursuits in software engineering. My studies focused on both theoretical knowledge and practical implementations, preparing me to tackle complex technical challenges effectively.
View CertificateRochester Institute of Technology – Institutional Research, Data & Analytics | May 2025 – Dec 2025
Completed a Dashboard Developer Co-op with RIT’s Institutional Research, Data & Analytics team, where I architected backend data services and analytics pipelines to support high-volume institutional reporting. Designed and delivered SQL- and Power BI–driven dashboards by translating stakeholder requirements into actionable insights, integrated heterogeneous data sources, and automated ingestion workflows to improve reporting reliability, ensure end-to-end data accuracy, and accelerate data-driven decision-making across the university.
Rochester Institute of Technology | Aug 2024 - May 2025
As a Course Assistant at RIT, I have significantly improved student outcomes by providing targeted instruction in software processes and development tools. Through my mentorship, tool setup time was reduced by 25%, enhancing project success rates by 30%. Additionally, my critical feedback and personalized support led to an 18% improvement in class performance, contributing to a 90% pass rate.
Axisray Pvt Ltd | Jan - May 2023
At Axisray Pvt Ltd, I worked as a Software Engineer Intern, developing and optimizing backend microservices using Java and Python within a distributed system. I built Flask-based REST APIs to enable reliable service-to-service communication and improved throughput, stability, and runtime performance across legacy services. I also revamped data workflows and automation pipelines, achieving measurable efficiency gains and reducing operational overhead, while enhancing backend maintainability through modular Spring Boot components and comprehensive unit testing.
Moon Technolabs Pvt Ltd | May - Oct 2022
During my internship at Moon Technolabs Pvt Ltd, I worked as a Data Science and AI/ML Engineering Intern, contributing to production-scale analytics and system integrations. I executed distributed data processing pipelines using Spark and C++ on AWS, achieving up to 2× faster processing for large workloads. I developed and unified Python-based service layers and REST APIs to integrate HRMS and CRM platforms, significantly improving cross-system data consistency and reliability. I also delivered multiple client-facing solutions end-to-end, collaborated with engineering teams on system architecture and rollout planning, and supported automated deployments that minimized downtime and improved overall release quality while reducing operational costs.
A quick snapshot of the tools, technologies, and practices I use to build scalable products.
Expert in building scalable and high-performance web and mobile applications using React, Spring Boot, Flask, and PostgreSQL.
Specialized in AWS, Azure, Kubernetes, Docker, and CI/CD Pipelines for efficient deployment, scaling, and automation.
Proficient in designing and managing relational and NoSQL databases like MySQL, PostgreSQL, and MongoDB for optimized data handling.
Building cross-platform mobile applications with React Native and integrating cloud services for seamless performance.
Expertise in unit testing, integration testing, and test automation to ensure software reliability and performance.
A group expense-splitting app built with React Native and Flask. It features real-time balances, receipt uploads (Textract), and group settlements using AWS services.
Chat with AI-generated fictional characters you create! Users provide traits, backstory, and personality, and the app handles dynamic conversations using GPT integration.
A mobile app for comparing product prices across online retailers. Built with React Native, includes user login, product search, wishlist, and data parsing from APIs.
A financial analytics platform deployed on AWS with Docker scalability. It integrates predictive analytics, boosting decision-making by 25%.
An AI-powered college recommendation system improving student advisement accuracy by 30% using Java, Flask, and MySQL.
A MERN Stack-based Crime Data Detection system that processes Los Angeles crime data from 2020 onwards. Crime records are ingested into MongoDB, cleaned, and geospatially formatted. Google Street View images of crime locations are fetched via API and stored in GridFS, with optimized retrieval through a custom endpoint. Enhances data visualization and investigative insight using real-time geolocation and image association.
A GitHub contribution graph visualizer powered by Neo4j and D3.js. It processes large-scale GitHub Archive data to uncover user–commit–repo relationships through a force-directed graph. The project features 3M+ nodes, custom Cypher queries, color-coded node types, and interactive commit exploration, helping visualize contribution patterns at scale with a sleek, browser-based interface.
An AI-enhanced exam authoring and difficulty calibration platform built with a cloud-native architecture. Examora integrates a React frontend with a Node.js/Express backend and AWS services including Cognito, DynamoDB, Bedrock, Textract, and SageMaker. Instructor-provided exam PDFs and topics are processed to extract key concepts, while a Playwright-based pipeline scrapes professor review data from RateMyProfessors and applies sentiment analysis to surface contextual difficulty signals. These inputs are combined to generate exam questions and rubric templates using LLMs, enabling faster, data-informed exam creation with human-in-the-loop review and control.
This research paper examines the 2017 Equifax data breach, a cybersecurity disaster that compromised the personal data of 147 million individuals. The study dissects the failure from a software architecture standpoint, identifying key issues such as an unpatched vulnerability in Apache Struts, poor security monitoring, and reliance on outdated legacy systems. The paper highlights how technical debt and inefficient security patching led to an extended period of unauthorized access. Proposed solutions include a shift to microservices, automated security patching, and Zero Trust architecture to mitigate future breaches. The case study serves as a cautionary tale for enterprises managing sensitive consumer data.
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This paper explores how legacy systems impact modern open-source development and proposes using design patterns to improve their maintainability, security, and scalability. The authors implement Factory, Observer, Strategy, and other patterns to reduce code complexity and enhance modularity. Empirical results using SonarQube and JaCoCo show measurable reductions in cyclomatic complexity across key modules of an online cab booking system.
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This review evaluates the DISTO metric introduced for assessing textual distractors in reading comprehension MCQs. The authors propose a novel approach using negative sampling strategies—such as answer replication, clustering, and BERT-based mask filling—combined with transformer-based models like DistilRoBERTa. The paper critiques traditional BLEU-like metrics and highlights DISTO's improved semantic sensitivity and human-aligned performance. Limitations around multilingual support, grammar validation, and computational scalability are discussed with future work directions.
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