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Adesoji A. - Ruby On Rails, React Native, nextjs

My name is Adesoji A. and I have over 3 years years of experience in the tech industry. I specialize in the following technologies: Deep Learning, Data Analytics & Visualization Software, Linux System Administration, Ruby on Rails, Amazon Web Services, etc.. I hold a degree in Master's degree, Bachelor of Engineering (BEng). Some of the notable projects I’ve worked on include: EnextAPI, Tenenx Data Processing and Visualization Platform, Youtube Summary, Car Image Classification, Cloth Color Detection, etc.. I am based in Kubwa Suburban District, Nigeria. I've successfully completed 6 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.

Main technologies

  • Ruby On Rails

    3 years

  • Deep Learning

    1 Year

  • Data Analytics & Visualization Software

    1 Year

  • Linux System Administration

    2 Years

Additional skills

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Experience Highlights

EnextAPI

Created an APi for Network telecommunication networks in Nigeria

Tenenx Data Processing and Visualization Platform

build an advanced platform that processes varied data types, such as documents and images, to extract semantic details and transform them into dynamic visual representations. This versatile system leverages the power of large language models (LLMs) to enable comprehensive analytics and interactive visualizations, including chatbot functionality for direct user engagement.

Youtube Summary

The service summarize any YouTube video by url. It works for long video and for English languages.

Cloth Color Detection

Fashion plays a significant role in modern human culture. More and more online fashion portals are popping up every day. But the main problem with them is that the clothes are manually labeled previously and are not properly attributed and arranged in the search engine which leads to mismatched search results for the customers. Manual labeling consumes a lot of time and labor. With the help of deep neural networks like CNN we can solve this problem since they are highly efficient in classifying images. I built a model for the detection and classification of clothes for eCommerce images. Using Yolo v4 and Residual Networks, these are the architectures used in this work for detection and classification respectively. I used a part of DeepFashion dataset, which contains box annotations for locations of clothes, and manually collected data for training and testing the clothes detection network and classification network. The model detect clothes using bounding boxes and further classifies the color of the detected clothing as seen in the attached picture.

Education

  • University of Lagos

    Master's degree in Engineering System Management

    2019-01-01-2021-01-01

  • University of Ilorin

    Bachelor of Engineering (BEng) in Electronic engineering

    2006-01-01-2012-01-01

Languages

  • English
  • Yoruba

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