AI Engineer specializing in LLMs, Retrieval-Augmented Generation (RAG), and NLP, with experience building production-ready intelligent document processing systems. Focused on developing enterprise-grade AI systems that d…
AI Engineer specializing in LLMs, Retrieval-Augmented Generation (RAG), and NLP, with experience building production-ready intelligent document processing systems. Focused on developing enterprise-grade AI systems that deliver measurable business impact. Skilled in Python, PyTorch, LangChain, FAISS, and deep learning, with experience developing scalable AI solutions including LLM + OCR pipelines, semantic retrieval systems, and machine learning models for real-world automation and analytics. Passionate about building practical AI applications that deliver measurable impact.
<p>Developed advanced recommendation systems using quaternion embeddings to improve sparse data representation and similarity learning.<br>
Architected and implemented a custom Quaternion Matrix Factorization model in PyTorch to enhance representation of sparse user–item interactions in recommendation systems.<br>
Developed and optimized Hamilton product-based embedding interactions using dual projection methods (radius and angle) to improve similarity learning.<br>
Optimized embedding dimensions (K=4–32), achieving peak performance with RMSE of 0.537 and MAE of 0.455.<br>
Engineered an end-to-end recommendation pipeline, including model training and Top-N recommendation generation.<br>
Leveraged PyTorch, NumPy, and efficient batching techniques to enable scalable large-scale prediction.</p>
<p>Developed an AI-powered document validation system using LLMs, OCR, and Retrieval-Augmented Generation (RAG) techniques to automate extraction and verification of structured banking data. Built an agentic RAG pipeline with semantic retrieval and embeddings, achieving 83.3% field-level extraction accuracy with an F1-score of 0.91 while reducing manual review effort by 40%. Deployed the system using Streamlit for real-time document processing.</p>
<p>Developed an advanced recommendation system using quaternion embeddings to improve sparse data representation and similarity learning. Implemented a custom Quaternion Matrix Factorization model in PyTorch, optimized embedding interactions using Hamilton products, and built an end-to-end recommendation pipeline including model training, prediction, and Top-N recommendation generation. Achieved strong performance with RMSE of 0.537 and MAE of 0.455 using scalable batching and efficient prediction techniques.</p>
The AI-first Recruitment Operating System that unifies and automates the entire hiring lifecycle for modern enterprises worldwide.
Dr Job FZ LLC. 2026 © All Rights Reserved
