Mohamed Mammar

Axibord Consulting is a company that develops web/mobile applications and AI solutions such as: AI Agents, Chatbots, facial recognition, text-to-speech, and fraud detection. In less than one year, I joined two projects as a Data Scientist: AzirAI and Speechit. As a Data Scientist my responsibilities included: - Implementing AI agent workflow pipelines using tools like LangGraph. - Development of agentic tools required to meet client needs - Deploying open-source models such as DeepSeek R1 using quantization methods (qLoRA / PEFT) locally and in the cloud. - Optimizing AI agents, thus reducing the latency by 30%. The website link for AzirAI: https://azirai.com The website link for Speechit: https://speechit.ai

Momentum Technologies is an IT consulting firm specializing in data, with over 20 years of experience. It supports Quebec organizations in managing their data and offers AI solutions to problems in various fields. The company has nearly 200 experts, with an estimated impact on more than 12 million users, and manages over 25,000,000 terabytes of data. Among their clients: Ramq and Ciusss. I joined the Data Scientist team at Momentum Technologies in 2022 to work on client and internal projects. As a Data Scientist, my responsibilities included: - Participation in AI initiative committees: market research, proposing innovative solutions, improving responses to calls for tenders. - Design and implementation of ETL pipelines, feature engineering, data exploration, visualization, and modeling. - Development of a chatbot with an LLM-RAG pipeline, capable of answering over 90% of questions from job seekers and recruiters. - Development of advanced AI fraud detection algorithms such as GBM, SVM, DBSCAN. - Implementation and maintenance of models developed on AWS services (S3, Lambda, SageMaker, Bedrock). I contributed to the following developments: - Momentum Technologies' revenue increased by +10%. - Extension of several contracts with large groups. - Automation of responses to various and diverse requests.

AGROLAB is a laboratory group active in the fields of agriculture and food safety/security with over 10 years of experience, specializing in microscopic image analysis for quality control and research. My responsibilities included: - Integration and processing of microscopic image data via ETL pipelines. - Development of Computer Vision pipelines using YOLO models to detect impurities (pollen, residues) in microscopic images of honey and food. - Fine-tuning of Vision Transformers (ViT-Base) and EfficientNet-B7 models to classify honey and pollen types and identify potential structural anomalies in protein cells. - Development of a U-Net architecture integrating attention mechanisms, specifically designed for precise segmentation of microstructures in images of food samples. - Model optimization through the application of classic and synthetic Data Augmentation techniques (StyleGAN-3) to improve the robustness and performance of segmentation. - Participation in the deployment of models on edge devices via ONNX Runtime and TensorRT, with optimization via pruning and quantization for connected microscopes, which drastically reduced inference latency. In less than a year, I was able to make major contributions to the development of time-consuming task automation while reducing their initial cost. I contributed to the following improvements: - AGROLAB's revenue increased by +15%. - 3 new clients (large groups) and 5 others. - Extension of several contracts with large groups. - Acceleration of the laboratory's scientific research, which increased by 25%.