UAE ML Engineer (Ads) – Real-Time Ranking & Fraud Detection

Noon; United Arab Emirates; On-site; AED 290,000 - 424,000; Full time

Job summary

Noon is seeking a Machine Learning Engineer 1 for its Ads division in Dubai. You will be responsible for hands-on design, training, and deployment of production-grade ML models powering ranking, bidding, and click-fraud detection.

The role requires strong Python skills, familiarity with standard ML libraries, and a foundation in ML concepts. You will collaborate with Product and Engineering teams to deliver high-impact ML solutions and follow ML Ops best practices.

Qualifications

• Hands-on experience or internships in Machine Learning or data science are preferred.
• Strong grounding in core ML concepts, statistics, and data structures.
• Proficiency in Python and familiarity with PyTorch, TensorFlow, Scikit-learn and SQL.
• Willingness to learn from senior engineers, collaborate with product and engineering teams, and ramp up on MLOps best practices.
• Basic exposure to cloud platforms (GCP) and data processing pipelines.

Responsibilities

• Deeply engage in building, training, and deploying production-grade models across our ads domains.
• Contribute to solving problems involving relevance, ranking, bidding optimization, and fraud detection.
• Partner with the broader engineering team to implement deployment strategies for heavy ML workloads.
• Adhere to and contribute to ML Ops standards and best practices within the team.

Skills

ML experience

ML concepts

Python + ML libs

Collaboration

GCP basics

Job description

Noon is seeking a Machine Learning Engineer 1 for its Ads division in Dubai. You will be responsible for hands-on design, training, and deployment of production-grade ML models powering ranking, bidding, and click-fraud detection.

The role requires strong Python skills, familiarity with standard ML libraries, and a foundation in ML concepts. You will collaborate with Product and Engineering teams to deliver high-impact ML solutions and follow ML Ops best practices.