Architecting production-grade AI systems — from multi-agent orchestration to LLM fine-tuning and scalable full-stack applications.
AboutMe
Academic Architecture
FAST-NUCES
Full-Stack Systems
Enterprise Scale
AI & Deep Learning
Neural Research
AI Engineer at OneScreen, architecting high-performance neural ecosystems where deep learning research meets advanced system architecture.
I direct AI engineering initiatives focused on scalable Multi-Agent Systems and high-performance neural architectures. My trajectory spans from building high-concurrency enterprise solutions to pioneering research in Generative Intelligence and Multi-Agent Orchestration.
ExperienceJourney
AI Engineer
Directing AI engineering initiatives focused on scalable intelligent systems and high-performance neural architectures.
Implementing advanced Multi-Agent Systems for enterprise automation
Developing high-performance systems with PyTorch and Transformer architectures
Optimizing deep learning models for scalable production deployments
Architecting neural ecosystems to solve complex business challenges
AI Web Development Intern
Architecting AI-powered web ecosystems and implementing high-efficiency automation protocols.
Built AI-powered web applications using Next.js 15 and TypeScript
Engineered workflow automation with n8n for distributed services
Developed CI/CD pipelines via GitHub Actions with 100% deployment integrity
Integrated multi-modal AI features leveraging Google Gemini
Selected
Works
Exploring the intersection of AI, engineering, and design through impactful projects.
FaceForge: Transformer-Based Face Synthesis & Detection
End-to-end deep learning framework combining a Vision Transformer generator (252M parameters) with an XceptionNet detector for facial manipulation synthesis and detection. Validated on FaceForensics++ with adversarial optimization and high-fidelity identity transfer capabilities.
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.
AI Music DeepFake Detector
Hybrid deep learning system combining Convolutional Autoencoders and Transformer Encoders to detect AI-generated music with 95% accuracy, 100% recall for authentic music, and perfect protection for human-created content.
Agentic OSINT Intelligence Platform
Production-ready multi-agent OSINT system that autonomously monitors global intelligence sources 24/7, processing 1000+ articles/hour through Kafka streams. Features temporal knowledge graph (Neo4j), NLI-based contradiction detection, credibility scoring, and real-time analytics dashboard.
Code-Morph: Autonomous Multi-Agent Repository Migration Engine
Cutting-edge autonomous system that transforms entire codebases across frameworks (TensorFlow→PyTorch) with zero logical drift. Features AST-driven semantic understanding, 5-agent orchestration, LLM-powered transformations, and automated verification proving behavioral equivalence.
Human vs. AI Text Classification
Comprehensive ensemble-based text classification system achieving 99.59% F1-score in distinguishing human-written from AI-generated text using 6 diverse classifiers and 4 advanced ensemble techniques.
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.
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.
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.
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.
Multi-Document Financial Analysis System Using RAG
Production-grade AI/NLP system for financial document analysis using Retrieval-Augmented Generation (RAG). Features LangChain integration, FastAPI REST API, multi-LLM support (Groq, OpenAI, Anthropic, Google, Cohere), and comprehensive testing with 45+ automated tests.
Skills
Arsenal
Engineering excellence from low-level systems to advanced neural architectures.
Programming Languages
10 technologiesAI & Machine Learning
8 technologies
Web Architecture
8 technologies
Cloud & DevOps
8 technologies
Databases & Systems
8 technologiesSpecialized Networks
6 technologiesCertified
Excellence
Professional credentials from Stanford, Coursera, and DataCamp in AI & Machine Learning.
Get In
Touch
Open for collaborations, projects, and technical discussions.
Direct Communication
nasirhuzaifa95@gmail.com
GitHub
github.com/Huzaifanasir95
linkedin.com/in/huzaifa-nasir