MLOps & AI Platform Engineer

Job Description: MLOps & AI Platform Engineer Job Title: MLOps & AI Platform Engineer Experience: 3 11 Years Location: Riyadh - Onsite Employment Type: Full-Time Job Overview We 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 Responsibilities • Design, build, and maintain enterprise-grade MLOps platforms and AI infrastructure. • Develop and automate end-to-end machine learning pipelines for training, validation, deployment, and monitoring. • Implement model versioning, experiment tracking, and model registry solutions. • Build scalable CI/CD pipelines for AI/ML workloads. • Deploy and manage machine learning workloads on Kubernetes-based environments. • Collaborate with Data Scientists, AI Engineers, Data Engineers, and DevOps teams to operationalize ML solutions. • Implement Infrastructure as Code (IaC) for cloud-native AI platforms. • Monitor platform health, model performance, and infrastructure availability. • Ensure platform security, scalability, reliability, and operational excellence. • Troubleshoot production issues and continuously optimize platform performance. Required Technical Skills MLOps Platforms • Hands-on experience with Kubeflow or Vertex AI Pipelines or SageMaker Pipelines . • Strong experience with MLflow for experiment tracking, model registry, and lifecycle management. • Experience orchestrating machine learning workflows using Apache Airflow . Containerization & Orchestration • Strong expertise in Kubernetes (GKE or AKS or EKS) . • Experience deploying and managing containerized AI/ML workloads in cloud environments. Infrastructure Automation • Hands-on experience with Terraform for Infrastructure as Code (IaC). • Experience automating infrastructure provisioning and cloud resource management. CI/CD & DevOps • Experience with GitHub Actions for CI/CD automation. • Knowledge of DevOps best practices, Git workflows, and automated deployments. Monitoring & Observability • Experience using Prometheus for infrastructure and application monitoring. • Knowledge of logging, alerting, and performance monitoring for AI platforms. Qualifications • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field. • 3 11 years of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI Infrastructure. • Strong scripting and automation skills using Python, Bash, or similar languages. • Excellent analytical and problem-solving skills. • Experience working in Agile/Scrum environments. Preferred Skills • Experience with Docker and containerized application deployment. • Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform. • Familiarity with model monitoring, drift detection, and automated retraining pipelines. • Experience implementing security best practices for AI/ML platforms. • Cloud and Kubernetes certifications are a plus. Key Technology Stack • MLOps Platforms: Kubeflow or Vertex AI Pipelines or SageMaker Pipelines • Workflow Orchestration: Apache Airflow and MLflow • Container Orchestration: Kubernetes (GKE or AKS or EKS) • Infrastructure as Code: Terraform • CI/CD: GitHub Actions • Monitoring: Prometheus • Cloud Platforms: Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred) • Automation: Python and Bash (Preferred) • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field. • 3 11 years of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering, or AI Infrastructure. • Strong scripting and automation skills using Python, Bash, or similar languages. • Excellent analytical and problem-solving skills. • Experience working in Agile/Scrum environments. Preferred Skills • Experience with Docker and containerized application deployment. • Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform. • Familiarity with model monitoring, drift detection, and automated retraining pipelines. • Experience implementing security best practices for AI/ML platforms. • Cloud and Kubernetes certifications are a plus.