Machine Learning Engineer

**HIRING**Job Title - Machine Learning Engineer Salary - AED 25,000 - AED 31,000 Per MonthContract Length - 1 Year (View to extend)Location - Abu Dhabi / Dubai Start Date - ASAPJob Purpose To design, build and operate secure, scalable and governed Machine Learning Operations (MLOps) capabilities that enable reliable deployment and lifecycle management of AI/ML models. The role is responsible for production-grade ML pipelines, cloud infrastructure, automation, observability and operational controls, with a primary focus on AWS. It works closely with Data Scientists, Data Engineers, Platform Engineering, Information Security, Model Risk and business stakeholders to accelerate model delivery while maintaining resilience, traceability, cost efficiency and regulatory complianceAccountabilities Design and implement scalable MLOps architectures, reusable deployment patterns and standard engineering components across development, testing and production environments, primarily on AWSBuild and maintain automated workflows for data preparation, training, validation, model registration, approval, deployment and retraining using orchestration and pipeline capabilities such as Amazon SageMaker Pipelines, Step Functions, Airflow or equivalent toolsImplement CI/CD for ML applications and infrastructure using Git-based workflows, automated testing, artifact management and Infrastructure as Code tools such as Terraform or AWS CloudFormationDeploy and operate batch and real-time inference services using appropriate patterns, including managed endpoints, containers, Kubernetes or serverless services. Ensure versioning, rollback, scalability and high availabilityImplement monitoring for model quality, data drift, service health, latency, throughput, failures and infrastructure. Establish alerts, dashboards, incident response and operational runbooksEngineer solutions using relevant AWS services such as SageMaker, S3, ECR, ECS/EKS, Lambda, Step Functions, CloudWatch, IAM, KMS and Secrets Manager, applying secure networking and least-privilege access controlsWhere applicable, configure and integrate Dataiku with enterprise data, AWS services and CI/CD processes. Support governed deployment, automation, monitoring and promotion of models across environments.Maintain model lineage, reproducibility, approvals, audit trails and technical documentation. Ensure adherence to data privacy, information security, Responsible AI, model risk and regulatory requirementsOptimise compute, storage and inference configurations for performance, resilience and cost. Implement tagging, usage monitoring, capacity controls and practical FinOps measuresPartner with Data Scientists, Data Engineers and application teams to remove productionisation blockers, improve engineering standards, review code and promote reusable MLOps practicesApply approved policies and procedures, continuously improve platform automation and support practices, and deliver responsive service to internal and external stakeholders.Demonstrate Our Promise and apply the ADCB Service Standards to deliver the Bank’s required levels of service in all internal and external customer interactionsMinimum Experience At least 7+ years of relevant experience in MLOps, ML engineering, DevOps, cloud engineering, platform engineering or production software engineering, including substantial hands-on responsibility for deploying and operating ML solutions in production.Strong hands-on AWS experience is required, particularly with Amazon SageMaker and associated services for storage, containers, orchestration, monitoring, identity and security.Proven experience building production-grade ML pipelines, CI/CD workflows, Infrastructure as Code, containerised deployments, monitoring and operational support.Experience with Dataiku is strongly preferred, especially platform integration, automation, deployment, administration or governed model lifecycle management.Experience in banking, financial services or another regulated environment is preferredKnowledge & Skills Advanced AWS and Amazon SageMaker engineering MLOps architecture and end-to-end ML lifecycle management Python and shell scripting; working knowledge of SQL CI/CD using GitHub Actions, GitLab CI, Jenkins, AWS Code Pipeline or similar toolsInfrastructure as Code using Terraform or AWS CloudFormation, Docker and Kubernetes, including Amazon ECR and EKS/ECS, MLflow, model registries, experiment tracking, and artifact managementBatch and real-time model deployment pattern Model, data and service monitoring; drift detection and observabilityDataiku platform experience and integration with AWS Cloud security, IAM, encryption, secrets management and secure networking Model governance, lineage, reproducibility, auditability and documentation Troubleshooting, incident management, performance tuning and cost optimisationStrong collaboration, stakeholder communication and technical documentation skillsMany Thanks,Ollie