MLOps Engineer (GCP) – Jeddah, Saudi Arabia

About the role

We are seeking an experienced MLOps Engineer with deep expertise in Google Cloud Platform to design, operationalize, and scale production‑grade AI/ML solutions. Based onsite in Jeddah, you will build reliable, secure, and automated end‑to‑end machine‑learning platforms that enable seamless collaboration across data science, AI engineering, and operations teams.

Key responsibilities

• Design and implement scalable MLOps architectures using GCP‑native services.
• Build standardized frameworks for model training, deployment, monitoring, retraining, and governance.
• Deploy and manage ML models with Vertex AI Endpoints for online and batch inference.
• Implement model versioning, rollout/rollback strategies, and traffic‑splitting for production deployments.
• Create and automate CI/CD pipelines for ML workflows and model deployment.
• Develop reproducible ML pipelines using Vertex AI Pipelines across dev, test, and prod environments.
• Integrate source control, testing frameworks, and artifact repositories into ML workflows.
• Monitor model performance, drift, data quality, and system reliability; set up observability, logging, and alerting.
• Define retraining triggers and support incident analysis for production ML services.
• Ensure scalability, security, compliance, and alignment with enterprise cloud architecture standards.

Required profile

• 5+ years of experience in ML Engineering, DevOps, MLOps, or related roles.
• Minimum 3+ years of recent hands‑on experience with Google Cloud Platform.
• Strong production experience deploying and managing ML systems at scale.
• Bachelor’s degree in Computer Science, AI, Data Science, or related field.
• GCP certifications (e.g., Professional Cloud DevOps Engineer) are a strong plus.

Required skills

• Google Cloud Platform (GCP)
• Vertex AI (Pipelines, Endpoints, Model Registry, Monitoring)
• CI/CD practices and infrastructure automation
• Docker and containerization concepts
• Python programming for ML workflows and automation
• ML monitoring, observability, reliability, and scalability
• Model versioning and deployment automation
• Source control (Git), testing frameworks, artifact repositories