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Schedule Interview NowMy name is Rakebun L. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Python, TensorFlow, Deep Learning, Neural Network, Natural Language Processing, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I've worked on include: AI-Powered Multimodal Document Assistant using Google Gemini API, Doc Insight — AI-Powered PDF Q&A Tool, Breast Cancer Segmentation using U-net Architecture, Multilingual Speech-to-Text Transcription using Wav2Vec2. I am based in Dhaka, Bangladesh. I've successfully completed 4 projects while developing at Softaims.
My passion is building solutions that are not only technically sound but also deliver an exceptional user experience (UX). I constantly advocate for user-centered design principles, ensuring that the final product is intuitive, accessible, and solves real user problems effectively. I bridge the gap between technical possibilities and the overall product vision.
Working within the Softaims team, I contribute by bringing a perspective that integrates business goals with technical constraints, resulting in solutions that are both practical and innovative. I have a strong track record of rapidly prototyping and iterating based on feedback to drive optimal solution fit.
I'm committed to contributing to a positive and collaborative team environment, sharing knowledge, and helping colleagues grow their skills, all while pushing the boundaries of what's possible in solution development.
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Adil Analytics Inc
Developed a multimodal AI tool capable of processing both text and image inputs, leveraging Google’s Gemini API for advanced reasoning. Built with Python, LangChain, and Streamlit, the app enables doc
Doc Insight is a lightweight AI application that transforms static PDF documents into interactive knowledge sources. Users can upload any PDF and ask natural language questions, receiving instant, acc
I developed a breast cancer segmentation model using U-Net and TensorFlow, achieving 98% accuracy. The project involved data cleaning & pre-processing, visualization, augmentation, hyper-parameter tun
I developed a multilingual speech-to-text transcription system using Wav2Vec2 and FastAPI. It supports multiple languages, including English, French, Italian, Spanish, and more. The system processes a
Bachelor of Science (BS) in Computer science
2016-01-01-2021-01-01