MLOps Engineer UAE

Quantum Talent Group is seeking an experienced, highly technical, and accomplished MLOps Engineer with a strong background in machine learning engineering and infrastructure automation to join our client project team on an urgent 12-month contract basis in Abu Dhabi, UAE, open to candidates who are already based in the UAE only. In this specialized artificial intelligence operations role, you will spearhead automating the machine learning lifecycle, deploying large-scale model training and inference pipelines, and ensuring robust model governance across cloud environments.

You will work closely with data scientists, AI researchers, and cloud infrastructure squads to bridge the gap between experimental data science and production-grade software delivery. Ideal candidates bring a robust academic background in computer science or artificial intelligence, deep practical mastery of ML pipelines, container orchestration, and immediate availability for deployment in Abu Dhabi.

As an MLOps Engineer at Quantum Talent Group in Abu Dhabi, UAE, you will take full ownership of building, scaling, and maintaining the infrastructure that powers enterprise AI and machine learning initiatives. Your day-to-day responsibilities encompass orchestrating model training workflows, implementing automated CI/CD pipelines for machine learning (MLOps), managing model registries, and monitoring model performance and data drift in production. You will utilize tools such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes to optimize inference latency, scale GPU resources, and enforce rigorous model governance and security standards.

Working in a fast-paced client-facing environment requiring UAE residency for a 12-month contract, you will collaborate with cross-functional teams to accelerate AI feature delivery.

• Design, build, and maintain scalable MLOps infrastructure and automated machine learning pipelines in production.
• Orchestrate end-to-end model training, validation, packaging, and deployment workflows using MLflow, Kubeflow, or Apache Airflow.
• Implement automated CI/CD pipelines for machine learning code, data validation, and model artifact management.
• Monitor model performance, data drift, concept drift, and inference latency in production environments.
• Collaborate closely with data scientists, AI researchers, and software engineers to transition models from research to production.
• Optimize model serving architectures, GPU utilization, and cloud compute costs across enterprise cloud platforms.
• Manage model registries, feature stores, and version control systems for datasets and machine learning models.
• Ensure strict adherence to AI governance, data privacy standards, and enterprise cybersecurity compliance baselines.
• Troubleshoot, debug, and resolve complex infrastructure, pipelining, and deployment failures.
• Maintain comprehensive technical architecture diagrams, operational runbooks, and MLOps documentation.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• Minimum 4+ years of professional software engineering experience, with at least 2+ years dedicated to MLOps and AI infrastructure.
• Strong programming proficiency in Python, Bash scripting, and modern software development practices.
• Deep practical experience with MLOps and workflow orchestration tools (MLflow, Kubeflow, Apache Airflow, or Feast).
• Solid hands‑on expertise with containerization (Docker) and container orchestration platforms (Kubernetes).
• Familiarity with cloud machine learning services (AWS Sage Maker, Azure Machine Learning, or Google Vertex AI).
• Strong analytical,…