Job Description

Roles & Responsibilities

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) Share

Desired Candidate Profile

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.