ML Engineer — Build & Deploy Real-World AI

DATAMAZE . AI; Dubai; Hybrid; AED 220,345 - 312,155; Full time

Job summary

DATAMAZE . AI is seeking a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team in Dubai. This mid-level position involves designing, building, and deploying innovative models for real-world challenges.

Ideal candidates will have 3–5 years of experience in machine learning, a relevant degree, and proficiency in programming languages such as Python and R. Responsibilities include model development, data preparation, and monitoring performance in production environments.

Qualifications

• 3–5 years of experience in machine learning or data science.
• Proven experience in building and deploying machine learning models.
• Experience in natural language processing, computer vision, or time-series analysis is a plus.

Responsibilities

• Design, train, and evaluate machine learning models.
• Perform data cleaning, preprocessing, and feature engineering.
• Deploy machine learning models into production.
• Research and implement state-of-the-art algorithms.
• Collaborate with data engineering teams to build data pipelines.
• Create visualizations and reports to communicate findings.
• Monitor model performance and optimize as necessary.
• Build tools for rapid experimentation.
• Maintain comprehensive documentation.

Skills

Python

R

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

SQL

Spark

Hadoop

AWS SageMaker

Google AI Platform

Azure ML

Matplotlib

Seaborn

Tableau

Education

Bachelor's or Master's degree in Computer Science or related field

Job description

DATAMAZE . AI is seeking a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team in Dubai. This mid-level position involves designing, building, and deploying innovative models for real-world challenges.

Ideal candidates will have 3–5 years of experience in machine learning, a relevant degree, and proficiency in programming languages such as Python and R. Responsibilities include model development, data preparation, and monitoring performance in production environments.