Lead ML Engineer - Scalable Production AI

Jex Recruitment | Connecting top talent with leading companies; Abu Dhabi Emirate; On-site; AED 450,000 - 650,000; Full time

Job summary

Jex Recruitment | Connecting top talent with leading companies is seeking a Lead Machine Learning Engineer for our Abu Dhabi AI Lab. You will design, develop and deploy production-grade ML systems, leading a high-performance team and shaping engineering standards.

You will own the ML lifecycle from experimentation through deployment and monitoring, collaborate with cross-functional teams, and drive best practices in MLOps and model reliability for scalable AI solutions.

Qualifications

• 8+ years of experience across ML, software engineering or related roles.
• Hands-on experience deploying ML models into production.
• Strong knowledge of ML algorithms, DL and modern AI architectures.

Responsibilities

• Lead the design, development, and deployment of production-grade ML systems.
• Build and optimise ML models across a range of AI use cases.
• Own the full ML lifecycle from experimentation to deployment and monitoring.
• Design scalable ML pipelines and infrastructure for training and inference.
• Collaborate with Data Scientists, ML Engineers, Software Engineers and Product teams.
• Drive best practices across MLOps, model performance, testing and reliability.
• Provide technical leadership and mentorship to engineers.
• Evaluate new models, frameworks and technologies and introduce them where appropriate.

Skills

Python development

Leadership

Mentorship

Model evaluation

Education

Bachelor’s or Master’s in CS/ML/AI

Tools

PyTorch

TensorFlow

AWS

Azure

GCP

CI/CD

Job description

Jex Recruitment | Connecting top talent with leading companies is seeking a Lead Machine Learning Engineer for our Abu Dhabi AI Lab. You will design, develop and deploy production-grade ML systems, leading a high-performance team and shaping engineering standards.

You will own the ML lifecycle from experimentation through deployment and monitoring, collaborate with cross-functional teams, and drive best practices in MLOps and model reliability for scalable AI solutions.