Machine Learning Engineer

Location: Dubai, United Arab Emirates
Job Category: Information Technology (IT) & Software; Science & Research; Engineering & Technical

Job Overview

An exciting opportunity is available for an experienced Machine Learning Engineer specializing in MLOps to support a major client in Dubai. The position is suitable for professionals who can take machine learning models from proof of concept to scalable production environments across cloud and edge platforms.

The role involves developing and deploying machine learning models and AI pipelines, implementing CI/CD workflows, monitoring model performance, and maintaining reliable production systems. The successful candidate will work with technologies including Python, PyTorch, TensorFlow, OpenCV, Scikit-learn, FastAPI, Docker, Kubernetes, MLflow, Kubeflow, Ray, and ClearML. Experience with messaging platforms such as Kafka or RabbitMQ and cloud environments such as AWS, Microsoft Azure, or Google Cloud is also relevant.

Candidates should have at least five years of professional experience and a strong understanding of machine learning deployment, model optimization, automation, and production monitoring. Immediate joiners are preferred, and applicants must currently be based in Dubai or elsewhere in the UAE. This role offers an opportunity to strengthen expertise in production machine learning, cloud-native infrastructure, and edge AI systems.

Key Responsibilities

• Develop, deploy, and maintain machine learning models and AI pipelines from proof of concept to production.
• Implement CI/CD workflows to streamline model testing, deployment, and updates.
• Build and maintain scalable machine learning infrastructure across cloud and edge environments.
• Monitor model performance and implement processes for model retraining to maintain accuracy.
• Optimize machine learning models using quantization, pruning, and knowledge distillation techniques.
• Prepare datasets, perform feature engineering, and support model tuning.
• Use containerization and orchestration technologies such as Docker and Kubernetes to deploy ML workloads.
• Work with model lifecycle and orchestration platforms such as MLflow, Kubeflow, Ray, and ClearML.
• Integrate machine learning services with APIs and messaging technologies such as FastAPI, Kafka, and RabbitMQ where required.
• Support deployment and optimization of machine learning workloads on GPU-enabled infrastructure using CUDA.
• Work with cloud platforms such as AWS, Azure, or Google Cloud.
• Contribute to observability, infrastructure automation, and reliable production operations.

Requirements and Qualifications

• Minimum five years of relevant professional experience.
• Must currently be based in Dubai or elsewhere in the UAE.
• Immediate availability is preferred.
• Strong programming skills in Python.
• Experience with machine learning frameworks such as PyTorch and TensorFlow.
• Familiarity with OpenCV and Scikit-learn.
• Experience deploying ML services using FastAPI, Docker, and Kubernetes.
• Knowledge of CI/CD pipelines and tools such as GitLab CI.
• Experience with MLflow, Kubeflow, Ray, or ClearML.
• Familiarity with Kafka, RabbitMQ, or similar messaging technologies.
• Understanding of model optimization techniques, including quantization, pruning, and distillation.
• Knowledge of CUDA and cloud platforms such as AWS, Azure, or Google Cloud is beneficial.
• Understanding of data preparation, feature engineering, model monitoring, and retraining processes.

Preferred Qualifications

The following skills are advantageous:

• Computer vision deployment on edge devices.
• Infrastructure as code.
• Observability and monitoring tools.
• Contributions to open-source projects.

Salary, Benefits and Career Growth

The employer has not specified the salary range, employment benefits, or certification support for this position. Candidates should confirm compensation and employment terms during the recruitment process.

The role offers opportunities to develop advanced skills in MLOps, model lifecycle management, production AI systems, edge computing, and cloud infrastructure. Professional development in machine learning engineering, Kubernetes, cloud platforms, and model deployment practices can support progression into senior MLOps engineering, ML platform engineering, or AI infrastructure architecture roles.