Umar A is a Full Stack AI Engineer with over two years of professional experience building production-ready AI systems, specializing in Agentic AI, deep learning, and high-performance AI backends. He has strong expertise in Python and modern AI frameworks, leveraging LangChain and LangGraph to architect autonomous, tool-calling workflows that automate complex business processes end to end. Umar is experienced in designing and deploying Retrieval-Augmented Generation (RAG) systems using embeddings and vector databases such…
Developed an autonomous recruitment agent that manages the end-to-end screening process using LangGraph for stateful, cyclic logic. Implemented tool-calling to allow the agent to query data and rank candidates based on specific job requirements. Integrated human-in-the-loop approval gates to validate agent decisions and ensure 100% accuracy before final shortlisting.
Built a Retrieval-Augmented Generation (RAG) pipeline to perform semantic search over internal PDF documentation, reducing information retrieval time for employees. Architected the system with a FastAPI backend, ensuring lowlatency responses and production-grade stability. Optimized retrieval relevance by implementing hybrid search and text-chunking strategies for complex PDF structures.
Engineered CLAHE-enhanced dental image pipelines with class-balanced augmentation. Fine-tuned YOLOv8L to achieve 87% mAP after benchmarking multiple object detection architectures on dental datasets. Delivered a cross-platform system (Web/Mobile) on AWS EC2/S3 and Supabase, enabling secure AI diagnostics via RBAC.
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Umar A is a Full Stack AI Engineer with over two years of professional experience building production-ready AI systems, specializing in Agentic AI, deep learning, and high-performance AI backends. He has strong expertise in Python and modern AI frameworks, leveraging LangChain and LangGraph to architect autonomous, tool-calling workflows that automate complex business processes end to end. Umar is experienced in designing and deploying Retrieval-Augmented Generation (RAG) systems using embeddings and vector databases such…
Developed an autonomous recruitment agent that manages the end-to-end screening process using LangGraph for stateful, cyclic logic. Implemented tool-calling to allow the agent to query data and rank candidates based on specific job requirements. Integrated human-in-the-loop approval gates to validate agent decisions and ensure 100% accuracy before final shortlisting.
Built a Retrieval-Augmented Generation (RAG) pipeline to perform semantic search over internal PDF documentation, reducing information retrieval time for employees. Architected the system with a FastAPI backend, ensuring lowlatency responses and production-grade stability. Optimized retrieval relevance by implementing hybrid search and text-chunking strategies for complex PDF structures.
Engineered CLAHE-enhanced dental image pipelines with class-balanced augmentation. Fine-tuned YOLOv8L to achieve 87% mAP after benchmarking multiple object detection architectures on dental datasets. Delivered a cross-platform system (Web/Mobile) on AWS EC2/S3 and Supabase, enabling secure AI diagnostics via RBAC.
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