Paula Ehab is an AI & Automation Engineer with 7 years of professional experience and 3 years focused on building and deploying NLP solutions across industries including banking, telecom, and government. She designs production-ready systems—chatbots, RAG document search, and IVR—using tools like LangChain, FAISS, Haystack, Kore.ai and cloud-native deployment patterns (Docker, FastAPI). Paula contributed to the widely used IVY framework by implementing TensorFlow frontend ops and precision fixes, and has shipped RAG systems that measurably reduced search time for users. Comfortable bridging product and engineering, she pairs strong stakeholder communication with hands-on model work (BERT, GPTs, Lora, Transformers) and practical prompt engineering. Based in Cairo, she brings a rare combination of conversational-AI platform expertise and low-level ML implementation experience spanning Python and .NET ecosystems.
7 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Helwan University Cairo
Contributions:66 reviews, 1 commit, 162 PRs in 1 day
Contributions summary:Paula primarily contributed to the `ivy` repository by implementing and testing machine learning functions related to the TensorFlow frontend. Their work involved adding and modifying functions within the TensorFlow framework, specifically focusing on `raw_ops`, math, and tensor operations. The user's contributions included adding functionalities like FFT, fixing and updating existing tests, and addressing issues related to data types and numerical precision.
Contributions:2 pushes, 1 branch in 3 years 6 months
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