I am a Computer Science graduate and Deep Learning Engineer specializing in building end-to-end AI pipelines, Computer Vision architectures, and Generative AI solutions. I focus on transforming complex data into fully automated, intelligent systems.

• Education: BS in Computer Science @ PAF-IAST
• Deep Learning & Vision: Experienced in architecting automated pipelines using state-of-the-art models (SAM 2, SegFormer, DCLGAN, Pix2Pix) for image segmentation and virtual staining.
• Medical AI Analysis: Proficient in complete data lifecycles—from pixel-by-pixel alignment and registration of raw data using QuPath and Fiji, to hyper-parameter optimization.
• Generative AI & LLMs: Exploring Agentic AI, custom fine-tuning (LoRA), and Retrieval-Augmented Generation (RAG) systems.
• Data Science: Highly skilled in Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).
• Software Engineering: Building robust mobile applications with Flutter utilizing MVVM architecture and SOLID principles.
• Contact: [email protected]

An AI-powered chatbot fine-tuned on FBR (Federal Board of Revenue) datasets, integrating LLaMA models with Retrieval-Augmented Generation (RAG).
It enables users to query tax regulations, policies, and data insights in natural language.
The project also demonstrates custom fine-tuning with LoRA adapters, data preprocessing, and real-time response generation.

Feature-rich mobile app engineered with clean MVVM architecture, STRICT adherence to SOLID principles, and optimal Provider state management.

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