Hi, I'm Omar Baig
I’m an AI Engineer and Researcher who builds technology that doesn't just process data—it understands context. My work sits at the intersection of Generative AI and privacy, where I develop everything from intelligent medical diagnostic tools to "preference-aware" AI agents. I’m driven by the challenge of making complex machine learning both scalable and secure, ensuring that the next generation of AI is as good as possible.
About Me
Get to know me better
I’m an AI Engineer and Researcher who builds technology that doesn't just process data—it understands context. My work sits at the intersection of Generative AI and privacy, where I develop everything from intelligent medical diagnostic tools to "preference-aware" AI agents. I’m driven by the challenge of making complex machine learning both scalable and secure, ensuring that the next generation of AI is as good as possible.
Experience
With a background spanning freelance engineering and corporate innovation, I’ve delivered range of full-stack AI projects, spanning from smart database querying systems to automated medical assessment tools. I’ve led award-winning team in computer vision and am currently pushing the boundaries of Federated Learning—a way for AI to learn from sensitive data without ever compromising user privacy.
Education
Bachelor of Science in Computer Science University of Engineering and Technology (UET), Lahore
Languages
Skills & Technologies
Core Concepts
Programming
Frameworks & Libraries
Deployment & Tools
Resume
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My Professional Resume
View my qualifications, experience, and skills
Experience
My professional journey
Generative AI Intern
- Led collaboration with developers and medical professionals to build an intelligent mobility assessment chatbot automating Functional Reach Tests using OpenCV and MediaPipe (95% accuracy).
- Engineered real-time pose estimation system achieving 90% validated measurement accuracy using geometric modeling techniques.
- Secured 1st position among 10+ competing teams
AI & Machine Learning Engineer
- Level 1 Seller on Fiverr.
- Delivered 10+ AI/ML projects including full-stack RAG-powered systems with FastAPI backend, React frontend, MongoDB, and VPS deployment.
- Built adaptive LLM-integrated databases for intelligent querying and automated decision support systems.
- Performed large-scale data analysis, trained and optimized ML models, and integrated generative AI solutions for real-world applications.
Education
My academic background
B.S. Computer Science
Thesis: AiroDx — Federated Context-Aware Explainable AI Framework for Multimodal Differential Diagnosis of Respiratory Diseases
Projects
Things I've built
AiroDx: Federated Explainable AI for Multimodal Respiratory Diagnosis
Developed a sophisticated diagnostic system that synchronizes spatial features from multiple X-ray perspectives using custom attention mechanisms and self-supervised ViT backbones. The pipeline automates the complex mapping of granular PadChest labels to target pathologies while managing massive datasets through efficient filename indexing and multi-worker loading. Optimized model convergence using a two-stage training strategy—transitioning from frozen feature extraction to full-model fine-tuning—to maximize per-class AUC and F1 scores.
RAG-Powered Medical Report Interpreter (SAAS)
Developed a full-stack AI SaaS platform that interprets medical lab reports using a 5-stage LLM pipeline (OCR → retrieval → analysis → trend detection → infographics), simplifying complex clinical data for patients and non-experts. Engineered a scalable and secure FastAPI backend with JWT authentication, async MongoDB, Pinecone vector search, rate limiting, and structured logging to deliver robust AI inference services. Deployed the solution with a Next.js frontend on Vercel and FastAPI backend on Hugging Face Spaces, providing a cloud-hosted service with file uploads, subscription management, and real-time report analysis.
Zero Trust Security in AI-Driven Healthcare Systems
Developed secure AI backend systems for healthcare predictions using a Zero Trust architecture to protect model integrity and data access. Implemented cryptographic verification and security mechanisms that significantly reduced simulated attack success rates.
Preference-Aware LLM Agents via Hierarchical Graphs and Federated Adaptation
Designed a preference-aware LLM agent that models user preferences using hierarchical graphs and dynamic LoRA adapter routing for personalized responses. The system incorporates federated continual learning to improve adaptation while maintaining privacy across distributed environments.
Jafs Gjerdrum
Jafs Gjerdrum is a comprehensive digital solution designed to modernize restaurant operations and enhance customer experience. This full-stack web application seamlessly integrates online ordering, real-time order management, and administrative controls into a single, user-friendly platform.
Hybrid Movie Recommendation System
Implemented a hybrid recommendation system combining transformer-based text embeddings with neural collaborative filtering for personalized movie suggestions. The model improved recommendation quality and addressed cold-start problems through integrated content and interaction signals.
AI-Powered YouTube Automation Pipeline
Developed a fully automated pipeline that generates motivational YouTube Shorts using LLM-based content generation, text-to-speech voiceovers, and stock footage from Pexels. The system programmatically blends video, captions, and audio using MoviePy and supports automated uploading or scheduling via the YouTube Data API.
Quora Question Pair Duplicate Detection
This project aims to determine whether a pair of questions from Quora are duplicates. Given a dataset of question pairs, various machine learning models were trained and evaluated to achieve the highest accuracy in predicting duplicate questions. The final model chosen was a Random Forest classifier due to its superior performance compared to other models.
Publications
Research & writings
Preference-Aware LLM Agents using Hierarchical Graphs and Federated Adaptation
Baig, O., Zafar, A., Ali, H., & Ali, A. Preference-Aware LLM Agents using Hierarchical Graphs and Federated Adaptation. International Conference on Machine Learning (ICML) (Under Review).
Certifications
Professional credentials
Supervised Machine Learning: Regression and Classification
Deeplearning.ai (Coursera)
Unsupervised Learning, Recommenders, Reinforcement Learning
Deeplearning.ai (Coursera)
Testimonials
What people say about me
“Working with Omar was an absolute pleasure! He’s professional, skilled, and incredibly responsive throughout the entire project. What impressed me most was his attention to detail and ability to truly understand my vision ,he not only delivered exactly what I wanted but even improved it with his own creative touch. Communication was smooth, deadlines were met without any issues, and the quality of the final result exceeded my expectations. I highly recommend Omar to anyone looking for a reliable and talented developer. I will definitely be working with him again!”
Kamal Dhesi
Visionary Architect · ViewAI
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