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)

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