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

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.

• 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 Dev Ops 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.

• Hands‑on experience with Kubeflow or Vertex AI Pipelines or Sage Maker Pipelines
  .
• Strong experience with MLflow for experiment tracking, model registry, and lifecycle management.
• Experience orchestrating machine learning workflows using Apache Airflow
  .

• Strong expertise in Kubernetes (GKE or AKS or EKS).
• Experience deploying and managing containerized AI/ML workloads in cloud environments.

• Hands‑on experience with Terraform for Infrastructure as Code (IaC).
• Experience automating infrastructure provisioning and cloud resource management.

• Experience with Git Hub Actions for CI/CD automation.
• Knowledge of Dev Ops best practices, Git workflows, and automated deployments.

• Experience using Prometheus for infrastructure and application monitoring.
• Knowledge of logging, alerting, and performance monitoring for AI platforms.

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field.
• 3–11 years of professional experience in MLOps, Dev Ops, 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.

• 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.

• MLOps Platforms:
  Kubeflow or Vertex AI Pipelines or Sage Maker Pipelines
• Workflow Orchestration:
  Apache Airflow and MLflow
• Container Orchestration:
  Kubernetes (GKE or AKS or EKS)
• Infrastructure as Code: Terraform
• CI/CD: Git Hub Actions
• Monitoring: Prometheus
• Cloud Platforms:
  Google Cloud Platform or Microsoft Azure or Amazon Web Services (Preferred)
• Automation: Python and Bash (Preferred)