ML Ops Engineer

Job overview

Miral Destinations is seeking an ML Ops Engineer to join its specialist AI team in Abu Dhabi. In this role, you will own the infrastructure, deployment, monitoring, and performance tuning that keep AI systems stable and effective in production.

You will work on the pipelines and platforms that move models from experimentation into secure, scalable live environments. The position reports to the Senior Manager AI and is focused on ensuring that operational delivery supports the AI roadmap and consistently provides dependable AI services.

Key responsibilities

• Set up and maintain the machine learning infrastructure and CI/CD pipelines that power AI solutions at Miral Destinations.
• Release models built by the Data Scientist – AI into production environments that are stable, scalable, and dependable.
• Track model quality, data drift, and system health, and troubleshoot issues affecting AI services.
• Improve infrastructure efficiency with an eye on cost, latency, and scale across AI workloads.
• Deliver day-to-day operational support and respond to incidents affecting production AI systems.
• Automate model retraining, version control, and deployment workflows using Databricks and MLflow.
• Use enterprise-wide platforms and shared AI capabilities in line with architecture and standards defined by the AI & Data organization.
• Maintain required security, governance, and compliance controls across AI operations.
• Work closely with AI, Data Engineering, BI, and Enterprise Data teams across DTD to reuse assets, follow shared practices, and leverage common platforms.
• Coordinate with enterprise platform and data engineering teams to keep deployment, monitoring, and operations consistent across DTD.

Requirements

• A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, AI, or a similar discipline.
• Three to five years of experience in MLOps, DevOps, or ML/data engineering roles.
• Practical experience putting machine learning models into production and supporting them there.
• Strong understanding of MLOps methods and how to deploy AI solutions at scale.
• Hands-on capability with Databricks, MLflow, and Python.
• Experience with CI/CD, Docker or Kubernetes, and infrastructure-as-code tools.
• Familiarity with model monitoring, observability, and drift detection.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Ability to optimize infrastructure for cost efficiency, low latency, and scalability.

Additional information

The job is based in Abu Dhabi Emirate, United Arab Emirates, and is a full-time onsite position. No stipend or salary figure was provided in the source.

No specific opening count, application deadline, start date, or internship duration was mentioned.

No separate perks, eligibility restrictions, or who-can-apply details were supplied beyond the education and experience requirements above.