Fresh Gravity, Analyst

Dec 2021 – June 2022 | Pune, India

Tecnoprism, Associate Software Engineer

June 2021 – Dec 2021 | Vadodara, India

Stevens Institute Of Technology, Candidate for Masters in Computer Science (MSCS)

Sep 2022 – May 2024 | Hoboken, NJ

GPA: 3.7

Courses: Cloud Computing, Agile Methods for Software Development, Artificial Intelligence, Data Analytics & Machine Learning, Database Management Systems

Gujarat Technological University, Bachelor in Computer Engineering

Jul 2017 – May 2021 | Anand, India

GPA: 8.39/10

Courses: Data Mining & Business Intelligence, Artifical Intelligence, Mobile communication and Networking, System Programming, Information & Network Security

MagicDot Solar

Technologies: Node.js, MongoDB, Handlebars, Docker, Kubernetes, Jenkins, Terraform, SonarQube, Prometheus

• Developed a comprehensive web application for Magicdot Solar, a solar energy solutions company. Leveraged a modern tech stack (Node.js, Express.js, MongoDB, Tailwind CSS) to create user-friendly dashboards for both employees and customers
• The application streamlines solar energy operations by encompassing functionalities such as task handling, customer support, sales, contract management, photo upload, emergency support, solar panel maintenance, and crew task management
• Orchestrated a robust CI/CD pipeline using Jenkins Declarative Pipeline, ensuring efficient development and deployment
• Designed and implemented various CI/CD stages, including automated builds, static code analysis for quality assurance (SonarQube), Docker image creation, and deployment to Kubernetes (AWS EKS)
• Leveraged ArgoCD for continuous delivery, automating deployments and maintaining consistency across environments
• Enhanced system visibility by configuring Prometheus and Grafana to monitor the Kubernetes cluster and Jenkins instance
• Employed Infrastructure as Code (Terraform) to automate infrastructure creation and provisioning on AWS

AWS Resource Manager

Technologies: React, Django, Terraform

• Built a full-stack web application using Django and React for automated AWS resource management, designed to optimize infrastructure utilization and reduce costs.
• Implemented a suite of cleanup tasks, including deletion of unused AMIs, EBS volumes, ECR repositories, ECS clusters, EKS clusters, IAM users, EC2 key pairs, RDS snapshots, and security groups based on customizable retention policies.
• Integrated automation to manage EC2 instances by tags, stopping instances as needed, and managing IAM access keys (create, disable, delete) to strengthen security operations.
• Created a module to remove specific ports from all inbound rules in security groups, improving network security.
• Added functionality for detecting infrastructure drift by parsing Terraform state files and AWS resources, ensuring consistent infrastructure states across environments.
• Designed intuitive user interfaces with React for resource selection, drift detection, and cleanup status, enhancing user experience and operational visibility.

AWSDriftGuard

Technologies: Python, Boto3, Terraform

• Created a CLI-based tool using Python to detect drift between AWS infrastructure and Terraform state files
• Designed a resource-drift detection engine that parses AWS resources (EC2, S3, RDS, IAM Roles, etc.) and compares them with Terraform state files to highlight discrepancies.
• Integrated Slack API for automated notifications, enabling seamless reporting of drift to relevant teams via Slack channels.
• Developed a comprehensive drift report generator in Python, providing clear and concise output for detected infrastructure drifts.
• Optimized tool functionality to be run in both "detect" mode (console output) and "report" mode (Slack integration)

Custom DNS Resolver

Technologies: Golang

• Developed a DNS resolver in Go with core functionalities for packet parsing, root server lookup, and iterative DNS queries.
• Implemented recursive querying to resolve domain names independently, without external DNS dependencies.
• Added robust error-handling for packet parsing and connection retries to ensure reliable DNS responses.
• Utilized Go's dnsmessage package for efficient message parsing and handling of A and NS record types.

Cloud Cost Optimization

Technologies: Python, AWS Lambda, Boto3, Cloud Watch logs

• Led a Cloud Cost Optimization initiative, reducing expenses by 25%
• Developed Python Lambda functions using Boto3 for automated AWS resource cleanup
• Integrated AWS CloudWatch Logs for real-time monitoring and analysis of cleanup processes
• Expanded optimization to cover various AWS services: RDS snapshots, S3 buckets, EC2 instances, and ECR images
• Introduced dynamic retention policies for targeted resource management
• Utilized AWS CloudWatch Events to trigger Lambda functions for timely execution

AWS Glue ETL Pipeline for NSW Property Data

Technologies: Python, AWS, Terraform, Redshift, Glue

• Built a data pipeline using AWS Glue and Terraform to extract, transform, and load (ETL) bulk property sales data into Redshift
• Automated the entire infrastructure setup with Terraform, including VPC, subnets, and security groups, improving scalability
• Processed nested ZIP files in DAT format, cleaned data for over 60000 separate files before storing in S3 for further processing
• Configured AWS Glue Crawler to infer the schema from the raw data stored in S3, set up the Glue Catalog for metadata storage
• Orchestrated AWS Glue jobs to perform the ETL from S3 to Redshift, optimizing the workflow for data processing

Credit Card Fraud Detection

Technologies: Python

• Balanced dataset using SMOTE for improved model performance
• Identified and addressed outliers to enhance model robustness
• Conducted EDA with Matplotlib and Seaborn for insightful visualizations
• Implemented Random Forest, Logistic Regression, KNN, and Decision Trees for fraud detection methodologies
• Achieved 86% accuracy, showcasing proficiency in fraud detection methodologie

Data Warehousing with AWS Redshift

Technologies: Python, SQL, AWS

• Built an ETL pipeline that extracts 1 million records from S3, stages them in Redshift, and creates dimension and fact tables.
• Configured AWS resources including Redshift clusters, IAM roles, and S3 buckets for secure and efficient data processing.
• Developed Python scripts to automate the extraction, transformation, and loading of data into Redshift.
• Implemented SQL scripts to create, drop, and populate tables in Redshift, optimizing query performance.

Automation of Code Maintenenace

Technologies: Ansible, Jenkins, AWS, Java, Maven, Tomcat

• Deployed an Ubuntu Server 22.04 LTS instance on AWS, configured for optimal performance
• Installed essential components, including Java, Jenkins, and Maven, to facilitate efficient software deployment
• Ensured secure communication between Jenkins, Tomcat, and Ansible instances
• Implemented Ansible playbooks to automate application deployment to Tomcat, enhancing efficiency and reducing manual errors
• Established a robust continuous integration and deployment (CI/CD) pipeline, streamlining software deployment processes
• Enhanced system security by configuring secure connections between AWS instances
• Achieved efficient application deployment to Tomcat through Ansible automation

pyFinance

Technologies: Python, Pandas, Matplotlib, Seaborn

• Developed a comprehensive Python module named Group_21 for financial data analysis and visualization, leveraging APIs from Yahoo Finance and NSE (India)
• Implemented functionalities to fetch historical stock data, calculate key valuation ratios (P/E, P/B, P/S, PEG), and export data in various formats including PDF, CSV, and Excel
• Designed and created multiple visualization tools such as line plots, candlestick charts, and technical indicators (Moving Averages, MACD, RSI, Volatility) using matplotlib and mplfinance
• Integrated session management and error handling to ensure reliable data fetching from NSE, enhancing the robustness and usability of the module
• Automated the process of generating detailed financial reports, allowing for seamless exportation and analysis of financial data for end-users

Phishing URL Detection

Technologies: Python, scikit-learn, pandas, NumPy, XGBoost

• Collected a dataset of phishing and legitimate websites from open-source platforms
• Developed a codebase to extract essential features from the URL database, including address bar, domain, and HTML & JavaScript-based features
• Conducted exploratory data analysis (EDA) to analyze and preprocess the dataset, dividing it into training and testing sets
• Implemented machine learning algorithms such as SVM, Random Forest, Naïve Bayes, XGBoost, Decision Trees, Multilayer Perceptrons, and KNN for classification
• Created evaluation code to display accuracy metrics and compared model outcomes to determine the most effective algorithm

AWS Certified SysOps Administrator – Associate

Issued: September 2024

Microsoft Certified: Azure Developer Associate (AZ-204)

Issued: July 2023

HashiCorp Certified: Terraform Associate (003)

Issued: April 2024

Microsoft Certified: Azure Data Fundamentals (DP-900)

Issued: March 2022

Microsoft Certified: Azure Fundamentals (AZ-900)

Issued: March 2022

AWS Fundamentals

Issued: February 2022