DevOps Projects in PunjabDevOps Projects in PunjabOnPrem .NET to AWS Migration
Migrated an on‐premises .NET web application and SQL Server database to AWS to achieve scalability, high availability, and cross‐region resilience. The legacy environment ran on Windows servers with a clustered SQL Server, resulting in high maintenance and limited elasticity. The new AWS architecture features EC2 Auto Scaling behind an Application Load Balancer (ALB), Amazon RDS for SQL Server with Multi‐AZ and a cross‐region read replica, and DNS failover via Route 53.
◦ Deployed the .NET application on an EC2 Auto Scaling group (Windows AMI) across private subnets in us‐east‐1.
5- Build Scalable Real-Time Data Analytics on AWS for Instant Insights
Designed and implemented a real-time data analytics platform on AWS, enabling stream processing, transformation, and visualization for high-volume IoT and log data. This system was built to process and analyze millions of records per second, providing actionable insights for business decision-making.
The solution leveraged AWS native services like Kinesis, Glue, Lambda, and Redshift, integrating Apache Spark Structured Streaming to handle real-time data ingestion and transformation. Airflow and Step Functions were used for workflow automation. Jewelry E-Commerce Website Description
I developed a modern full-stack jewelry e-commerce website designed to showcase premium jewelry with an elegant, luxurious, and responsive user experience. Customers can explore collections, view detailed product information, manage their cart, and place orders through a seamless shopping experience.
Key Features:
💎 Premium jewelry collections
🛍️ Product catalog with detailed product pages
🔍 Search, filtering, and category browsing
🛒 Shopping cart and checkout
👤 User authentication and account management
📦 Order management
🧑💼 Admin product and inventory management
📱 Fully responsive design
🗄️ Database-driven backend
🔐 Secure authentication and data handling
The project demonstrates my ability to build a complete full-stack e-commerce solution, from an elegant frontend and responsive UI to backend APIs, database integration, authentication, and order processing. This is my 5+ years of experience in an International company, Techlogix.
I currently serve as the Technical Lead of the Azure Cloud team, guiding engineers and driving complex cloud initiatives from design to deployment. My role spans end-to-end ownership of Azure solutions architecture, implementation, security, networking, DevOps practices, and operational excellence. I lead seven active projects and supervise a team of five engineers, ensuring high-quality delivery while maintaining strong coordination with cross-functional groups, including Development, QA, Data, and AI. I regularly engage with clients and external teams through direct calls, translating technical requirements into dependable cloud solutions.
One of my key strengths is resolving high-impact production issues that require deep technical insight. I am often the primary escalation point when traditional troubleshooting paths fail, and I take pride in restoring stability, quickly and precisely.
My academic background includes graduating as a university medalist with a CGPA of 3.72. In my professional tenure at Techlogix, I have been recognized for exceptional performance, earning the “Achiever of the Month” award and consistently ranking among the top-performing engineers.
I bring a combination of technical mastery, leadership, and a strong sense of ownership that aligns well with roles requiring responsibility, initiative, and cloud expertise.
My LinkedIn Profile Link: https://www.linkedin.com/in/muhammad-rameez-tahir-03545912b/ Rangoli CRM - Serverless React CRM Platform
Rangoli CRM was a serverless React.js CRM platform that I developed from scratch, including the frontend, serverless backend workflows, API integrations, deployment, and production setup. The system was built to manage customers, orders, billing, printable invoices, shipments, and business reporting from a single dashboard.
The frontend was built with React.js and hosted on Firebase Hosting. For backend/serverless logic, I used Firebase Cloud Functions, while Hasura was deployed on Heroku to provide GraphQL APIs for application data. I integrated shipment workflows, including Shiprocket, so the business could manage order delivery and tracking directly through the CRM.
My Role
Full Project Developer
Key Work
Developed the complete CRM from scratch
Built React.js dashboard screens for customers, orders, billing, invoices, shipments, and reports
Hosted the frontend on Firebase Hosting
Built serverless workflows using Firebase Cloud Functions
Used Hasura on Heroku for GraphQL data APIs
Integrated GraphQL APIs with Apollo Client
Integrated Shiprocket for shipment and delivery workflows
Managed frontend state using Redux
Built responsive UI using Ant Design
Handled deployment, debugging, and production setup
Tech Stack
React.js, Redux, Ant Design, Firebase Hosting, Firebase Cloud Functions, Hasura, GraphQL, Apollo Client, Heroku, Shiprocket API, JavaScript Time-Tracker Pro — Intelligent Time Management & Productivity SaaS
A comprehensive SaaS platform that helps teams and freelancers track time,
manage projects, and optimize productivity with AI-powered insights.
Core Features:
Real-time time tracking with automatic detection
Project & task management with hierarchical organization
Detailed time analytics and productivity reports
Team collaboration & billable hours tracking
Invoice generation from tracked time
Browser extension for seamless tracking
Mobile app for on-the-go time logging
Idle time detection & smart reminders
Integration with popular tools (Slack, Jira, Asana, Google Calendar)
AI-Powered Capabilities:
Automatic activity categorization using machine learning
Predictive project time estimates
Productivity insights & trend analysis
Smart recommendations for time optimization
Natural language project/task creation
Anomaly detection for unusual patterns
Dashboard & Reporting:
Real-time team activity dashboard
Customizable productivity reports
Time distribution charts & visualizations
Client billing reports with detailed breakdowns
Performance metrics & KPIs
Export to PDF, CSV, or integrate with accounting software
Technical Stack:
Frontend: React, Next.js, TypeScript, Tailwind CSS
Backend: Node.js, Express, NestJS
Database: PostgreSQL for relational data, Redis for caching
AI/ML: Python (scikit-learn, TensorFlow) for predictions
Real-time: WebSockets for live updates
Infrastructure: Docker, AWS EC2, S3, Lambda
CI/CD: GitHub Actions
Authentication: OAuth 2.0, JWT tokens
Key Accomplishments:
✓ 1000+ concurrent users support
✓ Sub-100ms API response times
✓ 99.9% uptime SLA
✓ Encrypted data storage & transmission
✓ GDPR compliant
✓ Scalable microservices architecture
✓ Automated testing (Jest, Cypress)
This project demonstrates expertise in:
SaaS architecture & scalability
Real-time systems & WebSockets
AI/ML integration for predictions
Payment processing & invoicing
User authentication & security
Team collaboration features
Production deployment & DevOps AI Image Forensics Web Application
Project Overview
A professional-grade, multimodal web application designed to detect and analyze AI-generated visual content. By combining traditional image processing techniques with advanced Large Vision-Language Models (VLMs), the platform provides comprehensive, automated analyses of image authenticity, digital signatures, and physical integrity.
Core Technologies & Frameworks
Next.js (React)
Python, FastAPI
Google Gemini Vision API
Docker, Google Cloud Run
Key Features & Engineering Highlights
8-Stage Forensic Pipeline: Engineered a highly robust image analysis pipeline that sequentially executes metadata scanning, quadrant tiling, Error Level Analysis (ELA), and Canny Edge detection.
Multimodal AI Analysis: Integrated Google Gemini Vision as an automated "forensic analyst" to evaluate visual inputs for physical inconsistencies, blending errors, and AI generation artifacts.
Decoupled Microservices Architecture: Built a high-performance Next.js frontend paired with a highly scalable Python/FastAPI backend to efficiently process complex mathematical image transformations.
Cloud-Native Deployment: Established automated workflows for containerizing the application with Docker and deploying the backend services to Google Cloud Run for scalable compute.
Data Visualization UI: Developed an intuitive, user-friendly dashboard to clearly present complex forensic findings, edge maps, and AI confidence scores to end-users. 
