AI Software Engineer

Muhammad Nouman Hanif

Building intelligent systems with machine learning, multimodal AI, and resilient cloud infrastructure. I focus on projects that turn real-world data into practical products.

Faisal Town, Lahore, Pakistan

Focus

Applied AI

Workflow

Research to Prod

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Featured Work

Selected AI and ML projects with real-world impact

A focused view of practical work across multimodal systems, computer vision, explainable AI, and predictive modeling.

Parameter-Efficient Fine-Tuning (PEFT) & Agentic Systems

Conducted a 42-run ablation study on DeBERTa-v3 using QLoRA, retaining 97% accuracy while reducing VRAM by 90%. Built a Multi-Agent Research Assistant using LangGraph to orchestrate agents for search, ranking, and summarization.

Multimodal Pipeline: Story Generation & E-Commerce Mockups

Engineered an end-to-end pipeline combining BLIP (Image Captioning) and FLAN-T5 (Story Generation). Built an automated product mockup system using Stable Diffusion (img2img), optimizing denoising strength for photorealistic textures.

Secure Face Anti-Spoofing System

Developed a robust biometric security system to detect face spoofing (replay/print attacks) using Transformer architectures. Fine-tuned Swin Transformer achieving 99.75% test accuracy on CelebA-Spoof.

Brain Tumor Classification with Explainable AI (XAI)

Engineered a multi-class classification model (Glioma, Meningioma, Pituitary) achieving 99.65% accuracy. Implemented Grad-CAM and Attention Rollout to visualize decision heatmaps.

Flood Severity Prediction with Ensemble Gradient Boosting

Engineered a robust flood forecasting pipeline using CatBoost and XGBoost, trained on a massive dataset of 1.1 million+ records. Achieved a high predictive accuracy of 0.85 R².

IoT-Enabled Smart Solar Inverter

Engineered an IoT-powered solar inverter with real-time Maximum Power Point Tracking (MPPT). Integrated a solar tracking system to enhance energy efficiency and power generation.