Machine Learning Engineer

Overview

The role involves designing, developing, and deploying machine learning models for development trends in the Arab region, focusing on data-driven insights and implementing LLM-based solutions.

Key Responsibilities

• Design, train, evaluate, and deploy supervised and unsupervised ML models.
• Implement and maintain ETL pipelines for data ingestion, transformation, and integration.
• Develop reproducible and version-controlled ML experiments.
• Apply feature engineering, model selection, hyperparameter tuning, and ensemble techniques.
• Explore and benchmark alternative large language models.
• Implement LLM-powered pipelines for document understanding, information extraction, and question answering.
• Explore and implement agentic solutions.
• Design and implement RESTful APIs to expose ML model outputs.
• Ensure model scalability, maintainability, containerization readiness, and thorough documentation.
• Collaborate with data engineers to align ML outputs with downstream requirements.
• Collaborate with data scientists, engineers, and domain experts.
• Prepare and contribute to technical reports, presentations, and documentation.

Required Experience

A minimum of 5 years of professional experience in machine learning engineering or a closely related discipline is required. Demonstrated proficiency in Python and core ML libraries (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) is required. Experience designing and deploying end-to-end ML pipelines in production environments is required. Knowledge of LLMs and experience integrating them into data or analytical workflows is required. Experience with MLOps practices, experiment tracking, and pipeline orchestration tools (MLflow, Airflow, Prefect, or equivalent) is desirable. Familiarity with cloud or containerized ML deployment environments (Docker, Kubernetes, or cloud ML platforms) is desirable. Experience with NLP tasks and multilingual or Arabic language models is desirable.

Qualifications

A bachelor's degree in computer science, data science, applied mathematics, statistics, or a related field is required. A master's degree in computer science, data science, applied mathematics, statistics, or a related field is desirable.

Other Details