MLOps & AI Platform Engineer

Job Description: MLOps & AI Platform EngineerJob Title: MLOps & AI Platform EngineerExperience: 3–11 YearsLocation: Riyadh - OnsiteEmployment Type: Full-TimeJob OverviewWe are seeking a skilled MLOps & AI Platform Engineer with 3–11 years of experience to build, automate, and manage scalable machine learning platforms and production AI environments. The ideal candidate will have hands-on expertise in MLOps, Kubernetes, cloud-native AI infrastructure, CI/CD automation, and model lifecycle management. You will be responsible for enabling data scientists and AI engineers to efficiently develop, deploy, monitor, and maintain machine learning models at scale.Key ResponsibilitiesDesign, build, and maintain enterprise-grade MLOps platforms and AI infrastructureDevelop and automate end-to-end machine learning pipelines for training, validation, deployment, and monitoringImplement model versioning, experiment tracking, and model registry solutionsBuild scalable CI/CD pipelines for AI/ML workloadsDeploy and manage machine learning workloads on Kubernetes-based environmentsCollaborate with Data Scientists, AI Engineers, Data Engineers, and DevOps teams to operationalize ML solutionsImplement Infrastructure as Code (IaC) for cloud-native AI platformsMonitor platform health, model performance, and infrastructure availabilityEnsure platform security, scalability, reliability, and operational excellenceTroubleshoot production issues and continuously optimize platform performanceRequired Technical SkillsMLOps PlatformsHands-on experience with Kubeflow or Vertex AI Pipelines or SageMaker PipelinesStrong experience with MLflow for experiment tracking, model registry, and lifecycle managementExperience orchestrating machine learning workflows using Apache AirflowContainerization & OrchestrationStrong expertise in Kubernetes (GKE or AKS or EKS)Experience deploying and managing containerized AI/ML workloads in cloud environmentsInfrastructure AutomationHands-on experience with Terraform for Infrastructure as Code (IaC)Experience automating infrastructure provisioning and cloud resource managementCI/CD & DevOpsExperience with GitHub Actions for CI/CD automationKnowledge of DevOps best practices, Git workflows, and automated deploymentsMonitoring & ObservabilityExperience using Prometheus for infrastructure and application monitoringKnowledge of logging, alerting, and performance monitoring for AI platformsQualificationsBachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field3–11 years of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI InfrastructureStrong scripting and automation skills using Python, Bash, or similar languagesExcellent analytical and problem-solving skillsExperience working in Agile/Scrum environmentsPreferred SkillsExperience with Docker and containerized application deploymentKnowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud PlatformFamiliarity with model monitoring, drift detection, and automated retraining pipelinesExperience implementing security best practices for AI/ML platformsCloud and Kubernetes certifications are a plusKey Technology StackMLOps Platforms: Kubeflow or Vertex AI Pipelines or SageMaker PipelinesWorkflow Orchestration: Apache Airflow and MLflowContainer Orchestration: Kubernetes (GKE or AKS or EKS)Infrastructure as Code: TerraformCI/CD: GitHub ActionsMonitoring: PrometheusCloud Platforms: Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred)Automation: Python and Bash (Preferred)