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hafsa batool

@hafsabatool

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AI Engineer | GenAI, Computer Vision, NLP & Deep Learning | Azure & AWS | IEEE Published

Pakistan

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What I'm looking for

I’m seeking an opportunity to design and deploy robust AI-based systems that leverage my technical expertise, research background, and interest in intelligent automation.

I am an AI Engineer specialising in LLMs, RAG, multi-agent systems, computer vision, NLP and deep learning. At AppLab Qatar, I build enterprise multi-agent chatbot platforms and RAG solutions on Microsoft Azure AI. Previously at Ezshifa, I developed healthcare AI models in PyTorch, deployed FastAPI services on AWS, and built an LLM-powered medical chatbot. At Diya GmbH in Germany, I worked on AI-driven route planning and driver-behaviour monitoring using computer vision.

I hold an MS in Computer Science, and my research on brain stroke lesion segmentation is published in IEEE Xplore. My interests include agentic AI, generative AI, healthcare AI and computer vision. I am looking for roles where I can build scalable AI products with real impact.

Experience

Work history, roles, and key accomplishments

Current

Software Developer

Current

Diya GmbH

Jun 2025 - Present (1 year 5 months)

Research and develop AI-driven systems for real-time route planning and fleet management. Developed computer vision models to monitor and classify driver behavior using object detection and classification.

AI Computer Vision Object Detection Classification Neural Networks Real Time Inference

AI Engineer

Ezshifa (Pvt) LTD

May 2022 - May 2025 (3 years)

Implemented deep learning models in PyTorch to predict patient vitals, converting models to ONNX for optimized deployment. Built containerized Flask REST APIs using Docker, deployed on AWS Lambda with integration to AWS S3.

PyTorch Onnx Flask Docker AWS Lambda S3 NLP OpenAI (LLMs)RAG Mobile App Deployment

Research Scholar

MID Lab, National Center of AI

Mar 2021 - Dec 2021 (9 months)

Proposed Patchwise Selective Augmentation (PWSA) to address class imbalance in brain stroke lesion segmentation. Designed a modified U-Net architecture for improved lesion segmentation accuracy.

Patchwise Selective Augmentation U Net Data Augmentation Brain Stroke Lesion Segmentation Research