MLOps Engineer

About the Role

We are collaborating with a prominent financial services company based in Abu Dhabi that is making substantial investments in advancing its artificial intelligence and data science capabilities. This position is for an experienced MLOps Engineer to join their AI innovation team. Your primary responsibility will be to architect and implement a robust machine learning platform to facilitate rapid model experimentation, seamless deployment, and scalable AI solutions throughout the organization.

Key Responsibilities

• Create and automate comprehensive machine learning pipelines that support continuous integration, delivery, and training (CI/CD/CT).
• Set up model serving frameworks capable of supporting both real-time ultra-low latency APIs (e.g., FastAPI, gRPC) and extensive batch inference jobs.
• Develop monitoring and logging systems in production environments to evaluate model accuracy, runtime latency, data drift, and concept degradation.
• Oversee model registries and manage metadata using platforms like MLflow or Kubeflow to ensure reproducibility, version control, and traceability of artifacts.
• Manage and scale containerized applications through Docker and production-grade Kubernetes clusters or managed cloud platforms such as EKS or AKS.
• Collaborate with security and compliance teams to guarantee pipeline encryption, network segmentation, secure IAM implementations, and adherence to regulatory standards.

Required Qualifications and Experience

• Between 3 to 7 years of practical experience developing and maintaining reliable machine learning pipelines and automation tools along with core platform infrastructure.
• Proficiency in programming languages such as Python, Go, or Java, supplemented by strong scripting abilities and advanced expertise in Linux operating systems.
• In-depth understanding of ML operational tools such as MLflow, Kubeflow, Argo Workflows, Feast, or cloud-managed equivalents like SageMaker or Azure ML.
• Practical knowledge of containerization with Docker and orchestration via Kubernetes, including Infrastructure-as-Code tools like Terraform and CloudFormation, and a solid grasp of cloud security concepts (e.g., VPC isolation, private endpoints, role-based access control, encryption).
• A minimum of a bachelor’s degree in Computer Science, Engineering, or a closely related discipline is mandatory; a master’s degree is preferred.

Benefits and Working Conditions

• Competitive salary package on offer.
• A four-and-a-half-day workweek complemented by 24 flexible workdays per year that permit remote work from any location.
• Comprehensive platinum health insurance covering the employee and their sponsored dependents.
• Opportunity to contribute to one of the largest AI innovation groups in the region, working on transformative projects employing leading-edge technologies in large-scale enterprise banking systems.