I build practical software at the intersection of AI/ML, full-stack engineering, and product-focused UI/UX.

My current focus is turning what I learn into deployable projects — from machine-learning applications and AI-assisted products to production-ready web applications.

Learning the fundamentals. Building real systems. Shipping continuously.

• 🤖 AI & Machine Learning — Python, NumPy, Pandas, scikit-learn, model development
• 🧠 Generative AI — LLM applications, RAG, embeddings, AI agents
• ⚙️ Backend Engineering — FastAPI, REST APIs, PostgreSQL, authentication
• 💻 Full-Stack Development — React, Vite, JavaScript, Tailwind CSS
• 🚀 Deployment & Engineering — Git, GitHub, Vercel, cloud-hosted applications
• 🎨 Product & UI/UX — building polished, responsive interfaces around real use cases

Full-Stack Learning & Productivity Platform

A production-deployed learning platform combining structured learning paths, focus sessions, analytics, streaks, achievements, authentication, and an AI learning companion.

Stack: React · Vite · Tailwind CSS · FastAPI · Python · PostgreSQL · Supabase · SQLAlchemy

Live: https://neuratrack-app.vercel.app/

Machine Learning Application

An end-to-end ML project that applies data preprocessing and machine-learning techniques to predict student performance through an interactive application.

Stack: Python · Pandas · NumPy · scikit-learn · Streamlit

Live: https://ml-student-performance-prediction.streamlit.app/

Modern Fashion E-Commerce Experience

A polished fashion e-commerce experience focused on premium visual design, responsive layouts, product presentation, and modern frontend interactions.

Stack: React · JavaScript · Vite · Tailwind CSS

Live: https://velora-atelier-e-com.vercel.app/

Premium Coffee Shop Web Experience

A responsive frontend project focused on visual storytelling, modern layouts, interaction design, and a refined brand experience.

Stack: HTML · CSS · JavaScript

• Building reliable RAG pipelines with embeddings and vector search
• Developing LLM-powered applications with useful product workflows
• Exploring AI agents and tool-using systems
• Strengthening machine-learning fundamentals and model evaluation
• Building scalable FastAPI backends for AI products
• Learning practical Docker and cloud deployment workflows

My long-term goal is to become an AI Engineer capable of taking an idea from data to a deployed product.

Problem
   ↓
Data → Model → API → Application → Deployment
                              ↓
                       Real User Value

I care about more than getting a model to run. I want to understand the engineering around it: data, evaluation, APIs, product design, deployment, and maintainability.

• 💼 LinkedIn: linkedin.com/in/wajeehaasad
• 🐙 GitHub: github.com/wajeeha-asad

Building in public. Learning by shipping. Improving one system at a time.