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Mariem M. AI, Machine Learning and MLOps

My name is Mariem M. and I have over 1 years of experience in the tech industry. I specialize in the following technologies: Python, MLOps, Machine Learning, Deep Learning, LLM Prompt Engineering, etc.. I hold a degree in Engineer's degree. Some of the notable projects I’ve worked on include: Facial Expression Recognition, Sentiment Analysis on IMDB Movie Reviews, Design and Implementation of a ML Pipeline for Invoice Validation, Forest Fire Prediction website. I am based in Tunis, Tunisia. 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

  • AI, Machine Learning and MLOps

    1 year

  • Python

    1 Year

  • MLOps

    1 Year

  • Machine Learning

    1 Year

Additional skills

  • Python
  • MLOps
  • Machine Learning
  • Deep Learning
  • LLM Prompt Engineering
  • OCR Algorithm
  • Object Detection & Tracking
  • 3D Image
  • Image Segmentation
  • AI-Enhanced Medical Imaging
  • Document AI
  • PyTorch
  • TensorFlow
  • MLflow

Direct hire

Potentially possible

Previous Company

Groupe MedTech

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Experience Highlights

Facial Expression Recognition

Used a pretrained CNN model and finetuned it in PyTorch to classify facial expressions. The data consists of 48 x 48 pixel grayscale images of faces and there are seven targets (angry, disgust, fear, happy, sad, surprise, neutral).

Sentiment Analysis on IMDB Movie Reviews

The main objective of this project was to determine the most effective combination of RNN architecture and word embeddings for classifying sentiments expressed in movie reviews as either positive or negative.

Design and Implementation of a ML Pipeline for Invoice Validation

Enhance financial management operations of the client.

Forest Fire Prediction website

This Forest Fires Detection project features a native web frontend and a powerful FastAPI backend, all hosted on Azure for optimal performance. Utilizing cutting-edge tools such as W&B (Weights & Biases) for monitoring and GitHub Actions for automation, this project showcases our commitment to innovation. Rigorous testing using pytest guarantees the highest quality. We've also developed and tested five distinct models, with the W&B dashboard assisting in performance comparisons. This was in collaboration with two other developers.

Education

  • Institut National des Sciences Appliquées et de Technologie (INSAT)

    Engineer's degree in Network and Telecommunications

    2020-01-01-2025-01-01

Languages

  • Arabic
  • German
  • English
  • French