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Schedule Interview NowMy journey at Softaims has been defined by curiosity, growth, and collaboration. I’ve always believed that good software is not just built—it’s carefully shaped through understanding, exploration, and iteration. Every project I’ve worked on has taught me something new about how to balance simplicity with depth, and efficiency with creativity. At its core, my work revolves around helping businesses and people achieve more through thoughtful technology. I’ve learned that the most successful projects come from teams that communicate openly and stay adaptable. At Softaims, I’ve had the opportunity to work alongside professionals who challenge assumptions, share knowledge generously, and inspire continuous improvement. I take pride in focusing on the fundamentals—clarity in logic, consistency in design, and empathy in execution. Software is more than a set of features; it’s a reflection of how we think about problems and how we choose to solve them. By maintaining this perspective, I aim to build solutions that are not only effective today but also flexible enough to support the challenges of tomorrow. The culture at Softaims promotes learning as an ongoing process. Every new project feels like a step forward, both personally and professionally. I see each challenge as a chance to refine my skills and contribute to the shared vision of building technology that genuinely improves lives.
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Created an APi for Network telecommunication networks in Nigeria
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.
The service summarize any YouTube video by url. It works for long video and for English languages.
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.
Master's degree in Engineering System Management
2019-01-01-2021-01-01
Bachelor of Engineering (BEng) in Electronic engineering
2006-01-01-2012-01-01