Machine Learning Engineer 1 – UAE National

The Machine Learning Engineer I helps build and maintain machine-learning components that support digital products and operational decisions. The role suits a UAE National with strong technical fundamentals who is ready to learn from senior engineers, work with data pipelines and develop reliable models and services. Attention to reproducibility, testing and maintainability is important as experimental code moves toward production use.

Key Responsibilities

• Prepare and validate datasets for model development, checking quality, consistency and potential data issues.
• Assist with implementing, testing and evaluating machine-learning models under the guidance of experienced engineers.
• Support integration of model outputs into applications or internal workflows and help investigate unexpected behaviour.
• Contribute to reproducible experiments, model evaluation records, monitoring and basic MLOps practices.
• Collaborate with product, data and engineering colleagues to understand requirements and communicate results clearly.

Requirements and Qualifications

• Meet the UAE National eligibility requirement for the position.
• Have foundational knowledge of programming, statistics, data handling and machine-learning concepts.
• Be willing to learn production software practices, model evaluation and responsible data use.
• Exposure to cloud platforms such as Google Cloud or to data-processing pipelines is beneficial.

Skills and Competencies

• Python or similar programming, data analysis, model evaluation, debugging and version-control fundamentals.
• Analytical curiosity, careful experimentation, collaboration and an interest in reliable production systems.

Employment Details

The role includes learning and collaboration as well as defined implementation tasks. The engineer should document experiments, ask for code review and focus on reproducible results rather than unsupported conclusions. The specific frameworks, cloud services, model types and training arrangements should be confirmed by the employer.

Implementation work should be tested and documented carefully so colleagues can understand decisions, reproduce issues and maintain improvements over time. The candidate should share progress early and raise dependencies before they affect delivery.