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Muhammad Uzair Khan
About
Detail
Lahore, Punjab, Pakistan
Résumé
Jobs
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- PSenior AI DeveloperPhaedra SolutionsSep 2025 - Current (10 months)• Architected and deployed highly scalable, AI-powered backend systems using FastAPI, integrating advanced LLMs (GPT-4, Gemini Pro) to power core enterprise applications. • Engineered multi-agent architectures and autonomous workflows that automated complex business processes, reducing manual operations by 40%. • Designed and implemented production-grade automation pipelines within n8n for automated AI document generation, intelligent data extraction, and webhook-driven event processing. • Developed high-performance vector search and semantic embedding pipelines, increasing the accuracy and relevance of internal data matching systems by 35%. • Optimized LLM performance, latency, and token allocation strategies across production environments
Lead Machine Learning EngineerOptimusFoxNov 2020 - Sep 2025 (4 years 11 months)• Led a technical team to design and build dynamic AI agents leveraging LangGraph and n8n, introducing seamless application integrations across corporate LLM services. • End-to-end engineered, evaluated, and deployed enterprise machine learning models on Azure Machine Learning, establishing robust, production-ready ML training and inference infrastructure. • Containerized and integrated ML models via production-grade REST APIs, enabling high-availability downstream consumption across multiple web and mobile applications. • Deployed enterprise Azure OpenAI (ChatGPT) solutions, adhering strictly to LLM prompt engineering and security best practices to protect sensitive data. • Streamlined IT and business operations by orchestrating serverless- LML Research ScientistLahore University of Management Sciences (LUMS)Nov 2017 - Jul 2019 (1 year 9 months)• Developed robust, multi-threaded web scrapers using Scrapy and Selenium to compile unstructured breast cancer patient data from globally distributed medical databases. • Architected data preprocessing and feature engineering pipelines for raw, highly skewed clinical datasets, ensuring clean data ready for advanced clinical modeling. • Evaluated and benchmarked classic Machine Learning classifiers, implementing optimized Support Vector Machines (SVM) on structured clinical datasets. • Pioneered Deep Learning approaches by applying Convolutional Neural Networks (CNNs) utilizing pre-trained VGG16 weights, improving cancer classification accuracy milestones.
Education
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- FBS in Computer Science (BSCS)FAST-NUCESJan 2013 - Jan 2017 (4 years 1 month)
Projects (professional or personal)
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- FSenior Capstone Project: Machine Learning Based Recommendation System for Android AppsFAST-NUCESJan 2013 - Jan 2017 (4 years 1 month)Engineered Python-based scrapers to mine application screenshots. Curated data pipelines and executed feature extraction via Deep Learning CNN layers to output user recommendation matches via similarity matrix mapping.
Publications
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- MMachine Learning Based App Recommendation System For Android AppsJan 2021
- IInfrared High Classification Accuracy Hand-Held Machine Learning Based Breast-Cancer Detection SystemJan 2019
- AA Portable Thermogram Based Non-Contact Non-invasive Early Breast-Cancer Screening DeviceJan 2018