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Schedule Interview NowMy name is Hamid A. and I have over 3 years of experience in the tech industry. I specialize in the following technologies: Data Analysis, Python, Microsoft Power BI, Data Science, Big Data, etc.. I hold a degree in Master's degree. Some of the notable projects I’ve worked on include: Log analysis using LLMs, RAG-for-PDF-question-answering, articles classification, retinal blood vessel extraction. I am based in Rabat, Morocco. 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.
Main technologies
3 years
2 Years
2 Years
1 Year
Potentially possible
IBM Morocco
In this internship project, we use LLMs to exploit logs generated by a payment application. During the project, we developed three main services. The first one reformulates errors generated by the system for client support, enabling them to effectively respond to user complaints. We then developed another service to summarize log sessions for developers. Finally, we parsed logs, extracted valuable information, and generated reports for the management team. Throughout the entire process, we used fine-tuning techniques and prompt engineering to achieve acceptable results.
The project involves the development of a RAG (Retrieval-Augmented Generation) system that uses data from multiple PDFs to answer user questions. The project was enhanced with a user interface for simple utilization
- The primary goal of our project was to develop a model capable of identifying criminal activities within news articles. By 'criminal activities,' we mean activities related to drugs, human rights violations, exploitation of children, sexual exploitation, terrorism, crimes against the state, smuggling, energy crimes, wildlife crimes, and any other unlawful activities. - Our responsibility involved gathering the necessary data, preparing it, and employing topic modeling techniques to construct a robust model capable of detecting criminal elements within the provided articles. - We achieved success in our project, managing to create a model with an impressive accuracy rate of approximately 95%, a noteworthy achievement in the realm of topic modeling projects
This project presents the work accomplished on a research project aiming to use deep learning for the segmentation of blood vessels in retinal images. The main objective of this project is to contribute to the early detection of ocular and systemic diseases that manifest through changes in the vascular structure. The core of the project is the design and implementation of a deep learning model using the U-Net architecture. The U-Net architecture was chosen for its specificity in image segmentation tasks, making it an ideal choice for our goal of blood vessel segmentation. Additionally, the U-Net architecture is particularly appreciated for its ability to handle disparities between the number of pixels in the object of interest and the surrounding context, which is commonly observed in medical images. In addition to the U-Net architecture, we also incorporated EfficientNet-B0 as the encoder in our model. EfficientNet-B0, known for its exceptional performance in image classification, provided additional capability to our model for accurately comprehending and analyzing complex medical images. By incorporating EfficientNet-B0, we enhanced the power of our model, enabling it to better recognize and segment vascular structures in retinal images. By combining these two powerful techniques, the U-Net architecture and EfficientNet-B0, we have successfully created a robust and accurate model. This model is capable of performing detailed analysis of medical images, identifying and segmenting blood vessels with high precision, thereby offering a potentially valuable tool for early disease detection. In summary, this project provides a detailed overview of the design, implementation, training, and deployment of a deep learning model for blood vessel segmentation in the medical field, highlighting the potential impact of these technologies in early disease detection."
Master's degree in Business intelligence and Analytics
2019-01-01-2022-01-01