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Schedule Interview NowMy name is Saher A. and I have over 10 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, Deep Learning, Keras, TensorFlow, TensorFlow Lite, etc.. I hold a degree in , . Some of the notable projects I’ve worked on include: KMP public library for voice recorder, Android & iOS applications in Kotlin Multiplatform, Correcting Spring Documentation Code Example, Optimizing Deep Learning Code. I am based in Bethlehem, Palestinian Territories. I've successfully completed 4 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
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Gaza Sky Geeks
KMP-Record is a lightweight Kotlin Multiplatform library designed to facilitate audio recording functionality across iOS and Android platforms. By abstracting platform-specific details, KMP-Record enables developers to manage audio recording in a unified manner, enhancing code reuse and maintaining consistency across platforms.
The project was a POS application, in KMP, it was developed with old framework that used an interactor not a viewmodel, that created an issue to expand the application. I solved the old framework issues, by expanding it to viewmodel, prepared the UI, implemented the logic in common code, implemented the API and database for both platforms.
There was a ready training code for an image recognition model, and it needed to be optimised to reach the needed accuracy percent (%85+). - I changed the optimiser - changed drop rate - changed neurons units - Filtered the data - created the needed colabs for the optimised code. - trained the model with the right number of epochs.
in Machine Learning
in Programming
2021-01-01-2021-01-01