Where It Started
I wrote my first line of code in 2019 — a simple C program that printed "Hello, World!" to the console. I was 16, sitting in a computer science class at Happy Palace School in Karachi, and I had no idea that this moment would define the next chapter of my life.
By the time I finished matric with a 98.12% score, I knew two things: I loved problem-solving, and I wanted to understand how machines could think.
Choosing AI at FAST NUCES
When I enrolled in the BS Artificial Intelligence program at FAST NUCES Karachi in 2021, AI was already transforming industries — but the curriculum was rigorous in a way that went far beyond the hype. Data structures, algorithms, linear algebra, probability theory, and statistics came first. The AI-specific courses — machine learning, deep learning, computer vision, NLP — built on top of that mathematical foundation.
My CGPA of 3.33 doesn't tell the full story. The real education happened in the projects: staying up until 3 AM debugging a neural network that wouldn't converge, discovering that your carefully crafted model fails on edge cases you never considered, learning that 85% accuracy means 15% of your predictions are wrong.
Competing and Growing
Competitions became my accelerator. Securing 2nd Place in the FAST Web Hunt Competition pushed me to learn React and Node.js under pressure — building a full-stack application in 48 hours teaches you more about development workflow than months of tutorials. The 3rd Place finish in the ACM Coders Cup sharpened my algorithmic thinking in ways that LeetCode problems (290+ solved, 6 badges) reinforced daily.
The Full Stack + AI Intersection
The most exciting space in tech right now isn't pure AI research or pure web development — it's the intersection. Building an ML model is one challenge. Deploying it behind a FastAPI endpoint, connecting it to a React frontend with real-time predictions, and making the whole system reliable, fast, and user-friendly — that's full stack AI engineering.
My Income Prediction System crystallized this philosophy. The ML model (85% accuracy on 32K records) was only half the project. The other half was the interactive analytics dashboard, the API design, the error handling, and the user experience that makes predictions interpretable.
What's Next
I'm currently open to AI/ML opportunities where I can combine my full-stack development skills with machine learning expertise. Whether it's building intelligent web applications, deploying ML pipelines, or creating AI-powered tools, I'm looking for roles where I can ship products that think.
If you're working on something interesting, let's talk.