ARCHITECT

HI, I'M

HUZAIFA
NASIR

AI Engineer

About Me

Tracing the strategic evolution from high-concurrency enterprise engineering to specialized Generative AI research.

Foundations

Academic Architecture

FAST-NUCES Study

Engineering

Full-Stack Deployment

Enterprise Systems

Research

AI & Deep Learning

Neural Specialization

I am an AI Engineer dedicated to architecting high-performance neural ecosystems where cutting-edge deep learning research meets advanced system architecture.

My professional trajectory is defined by a strategic evolution from building high-concurrency enterprise architectures to pioneering research in Generative Intelligence. I specialize in the multi-modal fusion of vision and language, leveraging Generative Adversarial Networks (GANs) and RAG architectures to push the boundaries of model performance, achieving extreme precision in biometric applications and optimizing neural synthesis for clinical and industrial impact.

My Experience

A record of specialized deployments in high-performance engineering and AI automation.

2025/07 – 2025/08

Nexium

AI Web Development Intern

Nexium_Terminal

2025/07 – 2025/08

Specialized in architecting AI-powered web ecosystems and implementing high-efficiency automation protocols.

/ Architected AI-powered web applications using Next.js 15 and TypeScript for specialized business logic.

/ Engineered intelligent workflow automation with n8n to synchronize distributed services.

/ Developed robust CI/CD pipelines via GitHub Actions, ensuring 100% deployment integrity.

/ Integrated multi-modal AI features leveraging Google Gemini for complex data synthesis.

/ Managed high-performance data layers with MongoDB and Supabase vector scaling.

/ Implemented modern design systems with Tailwind CSS following strictly optimized UX principles.

Next.jsTypeScriptAI/MLn8nVercelCI/CDGemini AISupabaseMongoDB

My Projects

A collection of projects showcasing my skills in various technologies and domains.

AI Virtual Try-On System

Deep learning-based virtual try-on system using multi-modal feature fusion (41 channels) and GANs to generate photorealistic garment transfer. Published research on Zenodo with comprehensive implementation.

Explore Project

Fine-tuning PubMedBERT for Medical Literature Embeddings

Domain-specific fine-tuned PubMedBERT model optimized for generating high-quality medical text embeddings using contrastive learning on 1,918 PubMed Central articles, achieving 0.78+ similarity scores.

Explore Project

Real-Time Sign Language Translator

Production-ready ASL recognition system achieving 99.60% accuracy with real-time performance (25-30 FPS) using ResNet18 and MediaPipe hand detection on consumer hardware.

Explore Project

PersonaClone: AI-Powered Conversational Persona Replication

Advanced AI system that clones conversational personas using RAG, OCEAN personality profiling, and LLM fine-tuning. Analyzes chat histories to generate authentic responses mimicking communication style and personality.

Explore Project

CLIP + LLM Image Captioner

Vision-language model combining CLIP's visual encoder with GPT-2 for automatic image captioning, achieving 97.8% training loss reduction with efficient transfer learning on Flickr8k dataset.

Explore Project

Comparative Analysis of TimeGAN and Diffusion Models for Synthetic Financial Time-Series Generation

Dual-objective research study evaluating TimeGAN vs Diffusion Models for synthetic data generation AND forecasting performance across 11 financial assets. TimeGAN achieves 54% better generation quality; ARIMA dominates forecasting with 97.51% accuracy.

Explore Project

CycleGAN: Face-Sketch Translation with Flask Web Interface

Implementation of CycleGAN for unpaired image-to-image translation between face sketches and photographs, featuring advanced Flask web application with 7-feature automatic input detection and real-time camera support.

Explore Project

Multimodal RAG System: Interactive PDF Chat with Vision & Text

End-to-end Retrieval-Augmented Generation pipeline processing 505 chunks from PDFs with hybrid Sentence-BERT + CLIP embeddings. Achieved MAP 0.253, Precision@1 62.5%, integrated LLaMA 3.2 via Ollama for local inference with 2.1s average response time.

Explore Project

Semantic Product Search and Ranking using BERT Embeddings

Neural ranking system for e-commerce using BERT embeddings and deep learning on Amazon ESCI dataset (2.68M query-product pairs). Achieved NDCG 0.9879, MAP 0.8707, F1 0.9287, significantly outperforming TF-IDF baseline with 87% top-1 precision.

Explore Project

Diffusion Transformers with REG Enhancement on CIFAR-10

Research implementation of Scalable Interpolant Transformers (SiT) enhanced with Representation Entanglement for Generation (REG) using pretrained DINOv2 models, with comprehensive comparison against U-Net baseline.

Explore Project

Neural Machine Translation: English-Urdu with mBART

State-of-the-art Neural Machine Translation between English and Urdu using mBART transformer model, achieving BLEU score of 75.98 on parallel corpus evaluation with 24,524 sentence pairs.

Explore Project

Fine-Grained Dog Breed Classification with ConvNeXt V2

State-of-the-art fine-grained visual classification using ConvNeXt V2 Base (88M parameters) with progressive training methodology, achieving 92.45% validation accuracy on 120 dog breeds from Stanford Dogs Dataset.

Explore Project

Fine-Grained Cat Breed Classification with ConvNeXt V2

Fine-grained visual classification using ConvNeXt V2 Base (88M parameters) with progressive training methodology, achieving 88.87% validation accuracy on 20 cat breeds from CatBreedsRefined-7k dataset.

Explore Project

PixelRNN: Autoregressive Image Modeling on CIFAR-10

Implementation and comparative evaluation of three autoregressive generative models (PixelCNN, Row LSTM, Diagonal BiLSTM) for image synthesis based on the seminal Pixel Recurrent Neural Networks paper.

Explore Project

CNN-CIFAR10: Comprehensive Image Classification with Hyperparameter Optimization

Complete CNN implementation for CIFAR-10 with extensive hyperparameter ablation studies, feature visualization analysis, and systematic performance evaluation achieving 95.2% accuracy.

Explore Project

Shakespeare Text Generation with LSTM-RNN

Word-level LSTM Recurrent Neural Network for next-word prediction and Shakespearean text generation using Tiny Shakespeare dataset with custom embeddings trained from scratch.

Explore Project