Hammad Ehsan Ullah - Backend Engineer | ContraWork by Hammad Ehsan Ullah
Hammad Ehsan Ullah

Hammad Ehsan Ullah

AI Engineer | Full-Stack Engineer I Blockchain Developer

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Hammad is ready for their next project!

Gujranwala, Pakistan

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Gujranwala, Pakistan

Cover image for Time-Tracker Pro — Intelligent Time
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
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Cover image for AI-Powered Application - LLM Integration
AI-Powered Application - LLM Integration & Intelligent Automation A production-grade application that leverages large language models (LLMs) to deliver intelligent automation, real-time assistance, and enhanced user experiences. Core Features: Multi-turn conversational AI with context awareness Retrieval-Augmented Generation (RAG) for knowledge-grounded responses Function calling for external API integrations Real-time streaming responses for better UX Prompt engineering for task-specific outputs Fine-tuned models for domain-specific use cases Vector database integration (Pinecone, Weaviate) Intelligent document processing & summarization Application Capabilities: Natural language understanding & processing Automated content generation Smart data extraction from unstructured text Intelligent customer support automation Code generation & debugging assistance Real-time translation & multilingual support Advanced search with semantic understanding Personalized recommendations Tech Stack: LLM APIs: OpenAI (GPT-4), Claude, Cohere Frontend: React, Next.js, TypeScript, Tailwind CSS Backend: Node.js, Express, FastAPI (Python) Vector Databases: Pinecone, Weaviate, Chroma Infrastructure: Docker, AWS, Vercel Monitoring: LangSmith, Helicone for LLM tracking Architecture Highlights: ✓ Asynchronous processing for scalability ✓ Caching strategies to optimize LLM costs ✓ Error handling & fallback mechanisms ✓ Rate limiting & usage monitoring ✓ Security: API key management, data encryption ✓ Performance: Sub-second response times ✓ Cost-optimized with token management This project demonstrates expertise in: LLM integration and orchestration Prompt engineering and optimization RAG pipeline development Production-grade AI systems Full-stack application architecture
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Cover image for DeFi Liquidity Protocol — Smart
DeFi Liquidity Protocol — Smart Contract Development A decentralized finance (DeFi) protocol enabling seamless token swaps, liquidity provision, and yield farming with secure smart contracts and intuitive user experience. Platform Features: Automated Market Maker (AMM) with dynamic pricing Multi-chain support (Ethereum, Polygon, Arbitrum, BSC) Liquidity pools with concentrated liquidity (Uniswap V3 style) Yield farming with reward distribution Governance token staking Advanced charting and price feeds Wallet integration (MetaMask, WalletConnect, Rainbow) Smart Contracts: Solidity development with ERC-20, ERC-721 standards Uniswap V2/V3 compatible architecture Gas-optimized code (~40% reduction) Comprehensive security testing & audits OpenZeppelin standard library integration Frontend & Backend: React/Next.js frontend with Wagmi & RainbowKit Node.js backend for indexing & analytics Real-time price feeds & market data PostgreSQL for transaction history TheGraph subgraph for on-chain data queries WebSocket connections for live updates Infrastructure: Docker containerization AWS deployment with auto-scaling CI/CD pipelines (GitHub Actions) Monitoring & alerting systems Tech Stack: Solidity, Web3.js, Ethers.js, React, Next.js, Node.js, TypeScript, PostgreSQL, Docker, AWS This project demonstrates expertise in DeFi protocols, secure smart contract development, blockchain integration, and scalable architecture.
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Cover image for VulnGuard AI - Smart Contract
VulnGuard AI - Smart Contract Vulnerability Scanner VulnGuard AI is an AI-powered smart contract security auditing platform that automatically detects vulnerabilities, security risks, and code quality issues in blockchain smart contracts. The platform leverages machine learning to identify patterns across thousands of contracts and provide actionable security recommendations. Built with Python, LLMs, and production-grade backend APIs for Web3 integration. Key Features: Automated vulnerability detection Machine learning-based pattern recognition Real-time security scanning Detailed risk reports with remediation guidance EVM-compatible blockchain support API integration for developer workflows This project demonstrates full-stack expertise in AI/ML, backend systems, blockchain security, and product development.
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Cover image for CopilotMeet — AI-Powered Meeting &
CopilotMeet — AI-Powered Meeting & Collaboration Platform CopilotMeet is a full-stack SaaS platform that enhances team meetings with real-time AI assistance, intelligent note-taking, and collaborative features. The platform provides: Real-time meeting transcription and AI-powered summaries Intelligent action item extraction and tracking Live collaboration tools for remote & hybrid teams Meeting analytics and insights dashboard Seamless calendar integration (Google, Outlook, Slack) Multi-participant support with role-based permissions Tech Stack: Frontend: React, Next.js, TypeScript, Tailwind CSS Backend: Node.js, Express, NestJS, PostgreSQL AI/LLM: OpenAI/Claude API integration for real-time summaries Infrastructure: Docker, AWS, CI/CD pipelines Real-time: WebSockets for live collaboration Key Features: ✓ End-to-end encrypted communications ✓ Scalable architecture for 1000+ concurrent users ✓ RESTful APIs for third-party integrations ✓ Mobile-responsive design ✓ Advanced search and meeting history ✓ Customizable meeting templates This project demonstrates expertise in full-stack development, LLM integration, real-time systems, and enterprise SaaS architecture.
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