MLOps Engineer

Job description

This is a chance to join one of the region's largest AI innovation teams and help build the platform that takes machine learning from experimentation into production at serious scale. You'll be joining a leading financial services organisation in Abu Dhabi that is investing heavily in AI and data, with a clear focus on building enterprise-grade systems rather than isolated proof-of-concepts.

The role sits at the heart of that journey.

You'll design and build the MLOps platform used by Data Science teams across the organisation, helping them move faster from model development through to deployment, monitoring and continuous improvement. That means building automated CI/CD/CT pipelines, creating infrastructure for both low-latency real-time inference and large-scale batch workloads, and making sure models can be deployed reliably and repeatedly. You'll also own areas such as model registries, lineage, versioning and production monitoring, using tools such as MLflow, Kubeflow and modern cloud-native platforms.

Working closely with Security, Data Governance and Engineering teams, you'll make sure the platform is built to the standards expected in a highly regulated environment, with strong controls around encryption, IAM, network isolation and auditability.

• 3-7 years' experience building and operating production ML or MLOps platforms.
• Strong programming experience in Python, Go or Java.
• Hands-on experience with tools such as MLflow, Kubeflow, Argo Workflows, Feast, Sage Maker or Azure ML.
• Strong knowledge of Docker, Kubernetes and Infrastructure-as-Code tools such as Terraform or Cloud Formation.
• Experience building and supporting real-time and batch inference environments.
• Good understanding of cloud security, including VPC isolation, RBAC, private endpoints and encryption.
• Strong Linux and systems engineering knowledge.
• Bachelor's degree in Computer Science, Engineering or a related field. A Master's degree would be advantageous.