Mohammed Haitham

• Designed and developed AI models to provide predictive insights for maternal
health and pregnancy care.
• Implemented data preprocessing pipelines and feature engineering to enhance
model accuracy and reliability.
• Collaborated with the healthcare team to align AI outputs with clinical
requirements, improving patient outcome predictions.

• Contribute to ML/LLM prototypes and model evaluation for product features.
• Implement data preprocessing pipelines and write clean, testable Python.
• Collaborate across engineering to scope tasks and track experiments.
Tech: Python, PyTorch, scikit-learn, pandas, NumPy.
• Built and tested automation workflows using n8n for daily LinkedIn posting and
content creation.
• Used Copilot Studio to create simple AI agents and prompt flows for internal
tasks.
• Connected n8n with Google, LinkedIn, and OpenAI and solved basic credential
and OAuth issues.
• Tried different ready-made templates in n8n and improved them for the teams
needs.
• Helped explore new AI tools that could be installed on the company server.

• Contribute to ML/LLM prototypes and model evaluation for product features.
• Implement data preprocessing pipelines and write clean, testable Python.
• Collaborate across engineering to scope tasks and track experiments.
Tech: Python, PyTorch, scikit-learn, pandas, NumPy.

• Build and iterate on LLM/RAG proof-of-concepts and retrieval pipelines.
• Use Python for data processing, feature engineering, and model
experimentation.
• Assist with vector store integration and prompt/agent design for conversational
flows.
Tech: Python, RAG, Vector DBs, Prompt Engineering.

• Processed and cleaned product image datasets for use in machine learning pipelines.
• Engineered visual features from images to support similarity detection and classification.
• Assisted in designing and training deep learning models for product image recognition and retrieval.
• Contributed to building a recommendation system that suggests visually similar products from uploaded images.
• Participated in regular code reviews and model validation meetings with the AI team.
• Documented training workflows, model performance, and deployment steps using Jupyter and GitHub.