Machine Learning Engineer

Role overview

What you will do

• Qualify and prioritize client use cases, then build, deploy, and monitor models.
• Perform statistical analysis and explain results to non-technical audiences.
• Design and build Arabic and English dashboards and automated reports.
• Use only masked or de-identified training data and document the features and data used.
• Maintain models and dashboards through retraining, drift monitoring, scheduled refreshes, and performance checks.
• Help write monthly progress reports for AI and dashboard work.

Required qualifications

• Use Python, pandas, scikit-learn, and at least one of PyTorch, TensorFlow, or XGBoost; have run models in production.
• Work with forecasting, segmentation, recommendation, and NLP use cases.
• Apply MLOps practices for reproducible training, deployment, monitoring, and retraining.
• Conduct statistical analysis and design experiments.
• Use Qlik Sense load scripts, associative model, set analysis, section access, and report automation.
• Use Power BI data models, DAX, Power Query, row-level security, and scheduled refresh.
• Build Python reports and visualizations with Plotly or Dash, Streamlit, or similar.
• Design bilingual, right-to-left dashboards and use privacy-aware masking and de-identification practices.

Additional desirable skills

• Arabic NLP, Vertex AI, or BigQuery ML is a plus.
• Marketing-mix, attribution, or uplift modelling is a plus.
• Qlik or Power BI certification, or Google Professional Machine Learning Engineer certification, is a plus.