Faraz Mirza  

Engineered an autonomous multi-agent ML orchestration platform that compresses 2-4 weeks of manual ML pipeline development into 30 seconds using LangGraph's stateful orchestration.

• Architected 4 specialized agents working in parallel:
  • DataEngineer: Handles missing values & normalizes numerical columns
  • MLArchitect: Generates production-ready scikit-learn code with train/test splits
  • AdversarialQA: Validates code for ML best practices (data leakage detection, 3-iteration self-correction loop)
  • BusinessAnalyst: Translates technical metrics to business-friendly executive reports
• Built FastAPI backend with real-time Server-Sent Events (SSE) streaming for live agent progress updates to frontend
• Implemented intelligent code extraction using regex (re.DOTALL) with 98.5% success rate on LLM outputs
• Created React + TypeScript + Tailwind CSS frontend with smooth animations, skeleton loaders, and one-click code copy
• Deployed locally with Ollama (Qwen 2.5 Coder 3B) for zero per-request costs and privacy-first execution

Links: GitHub

Engineered a full-stack AI-powered phishing detection system analyzing URLs in real-time with 95%+ accuracy using a hybrid whitelist + deep learning approach.

• Built an interactive cyberpunk-themed UI with React 18, TypeScript, and Tailwind CSS v4, featuring real-time URL scanning with explainable AI analysis reports
• Architected a RESTful API with FastAPI and Python 3.10+, implementing a hybrid detection engine that combines whitelist validation with deep learning for millisecond-latency threat analysis
• Designed a custom "Y-Network" architecture using TensorFlow/Keras combining Bi-LSTM layers (for URL text sequences) and Dense layers (for 12 lexical features) with scikit-learn preprocessing pipelines

Links: GitHub

Engineered a professional-grade AI forensic system detecting deepfakes and AI-generated media with 92.5%+ accuracy using a Triple-Agent architecture combining machine learning with digital forensics.

• Built an interactive cyberpunk-themed UI with React 19.2, TypeScript, and Tailwind CSS v4, featuring real-time forensic analysis logs with three parallel detection agents
• Architected a RESTful API with FastAPI running three specialized agents in parallel:
  • Vigilante-V2: Face swap & traditional deepfake detection using ViT (Vision Transformer)
  • Sentinel-X: AI-generated & synthetic media detection for GAN artifacts
  • Prism: Digital forensics engine combining EXIF metadata scanning, ELA (Error Level Analysis), and MediaPipe facial geometry validation
• Designed a sophisticated detection pipeline using MAX confidence logic, OpenCV for image processing, and Docker containerization for production deployment

Links: GitHub

Engineered a full-stack AI-powered fashion aesthetic classifier analyzing outfit images in real-time with 5-class deep learning categorization using a fine-tuned ResNet18 architecture.

• Built an intuitive drag-and-drop UI with React 19.2, Tailwind CSS v4, and Vite, featuring real-time image classification with detailed confidence score breakdowns and probability distributions
• Architected a RESTful API with FastAPI and Python 3.9+, implementing an optimized inference engine that combines image preprocessing pipelines with millisecond-latency style predictions
• Designed a transfer learning architecture using PyTorch 2.5.1 and torchvision, fine-tuning ResNet18's final fully-connected layer for 5-class fashion classification (Goth, Gym Rat, Old Money, Streetwear, Y2K) with softmax probability outputs

Links: GitHub

• 🔐 Password-protected entries - your privacy matters
• 🌙 Dark & light themes - write whenever you're in the mood
• 💾 Local storage - your data stays on your computer
• ✍️ Handwriting-style fonts - makes journaling feel personal
• 📱 Clean, distraction-free interface