MLOps Architect / Engineer (0–12+ Years Experience)

Please read the JD carefully berore applying.Job Description – MLOps Architect / Engineer (0–12+ Years Experience)PositionMLOps Architect / EngineerLocationRiyadh, Kingdom of Saudi Arabia (KSA)Relocation Required: YesExperience0–12+ YearsJob SummaryWe are seeking an experienced MLOps Architect / Engineer to design, build, and operate enterprise-grade Machine Learning Operations (MLOps) platforms. The ideal candidate will define and operationalize scalable ML platforms while automating the complete machine learning lifecycle, including data preparation, model training, versioning, deployment, monitoring, governance, and automated retraining.The role requires expertise in cloud-native MLOps, Kubernetes, CI/CD automation, Infrastructure as Code (IaC), and enterprise AI platform engineering to enable reliable, secure, and scalable production AI solutions.Key ResponsibilitiesDesign and implement enterprise MLOps architecture supporting the complete machine learning lifecycleBuild automated ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoringDevelop scalable CI/CD pipelines for machine learning models and AI applicationsManage model versioning, experiment tracking, model registry, and artifact managementDeploy ML workloads on Kubernetes-based environments with high availability and scalabilityImplement automated model monitoring, drift detection, performance tracking, and alertingDesign automated retraining pipelines based on model performance and data driftStandardize ML platform governance, security, reproducibility, and operational best practicesCollaborate with Data Scientists, Data Engineers, AI Engineers, DevOps, and Cloud teams to accelerate AI solution deliveryOptimize infrastructure utilization, deployment automation, and platform reliabilityDevelop Infrastructure as Code (IaC) for cloud-based AI platformsEstablish enterprise monitoring, logging, observability, and incident response for ML workloadsDocument platform architecture, operational standards, deployment procedures, and recovery processesRequired Technical SkillsMLOps PlatformsKubeflow or Vertex AI Pipelines or SageMaker Pipelines or MLflowWorkflow OrchestrationApache AirflowContainerization & OrchestrationKubernetes (GKE or AKS or EKS)Infrastructure as CodeTerraformCI/CD & DevOpsGitHub Actions and Git and CI/CD PipelinesMonitoring & ObservabilityPrometheus and Model Monitoring and Drift DetectionProgrammingPython and BashCloud PlatformsGoogle Cloud Platform (GCP) or Microsoft Azure or Amazon Web Services (AWS)Version Control & AutomationGitHub or GitLab or Azure DevOpsResponsibilities by Experience Level0–3 YearsSupport deployment and monitoring of ML modelsBuild and maintain ML pipelines under senior guidanceAssist with CI/CD implementation and platform automationLearn Kubernetes, cloud platforms, and Infrastructure as Code3–6 YearsDevelop production-grade MLOps pipelinesImplement model versioning, monitoring, and deployment automationManage Kubernetes-based ML workloadsBuild Infrastructure as Code using TerraformImprove platform reliability and operational efficiency6–9 YearsLead enterprise MLOps implementationsDesign scalable AI platforms across cloud environmentsStandardize CI/CD, governance, monitoring, and operational processesMentor junior engineers and collaborate across engineering teams9–12+ YearsOwn enterprise MLOps strategy and platform architectureDefine standards for AI platform engineering and lifecycle automationLead large-scale AI platform modernization initiativesDrive governance, security, scalability, and operational excellenceProvide technical leadership across enterprise AI and cloud engineering teamsPreferred CertificationsOne or more of the following certifications is highly preferred:Certified Kubernetes Administrator (CKA)Kubeflow Certified ProfessionalGoogle Professional Machine Learning EngineerMLflow CertificationDatabricks Certified MLOps ProfessionalExpected DeliverablesEnterprise MLOps Architecture DocumentEnd-to-End CI/CD Machine Learning PipelineProduction Model RegistryModel Drift Monitoring & Alerting FrameworkAutomated Retraining PipelineInfrastructure as Code (Terraform) RepositoryKubernetes Deployment TemplatesML Platform Operational RunbookModel Lifecycle Governance FrameworkMonitoring & Observability DashboardPreferred QualificationsBachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related disciplineStrong understanding of machine learning lifecycle management and production AI systemsExperience designing cloud-native AI platforms using Kubernetes and Infrastructure as CodeExcellent problem-solving, collaboration, and technical leadership skillsAbility to work in enterprise-scale, cross-functional, and agile environments