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, deploy and operate production‑grade AI/ML solutions. Based in Jeddah, Saudi Arabia, you will build reliable, secure and automated end‑to‑end machine‑learning platforms that enable seamless collaboration between data scientists, AI engineers 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.
• Create and automate CI/CD pipelines for ML workflows and model deployment.
• Develop automated ML pipelines using Vertex AI Pipelines ensuring reproducibility across 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 and remediation of production ML services.
• Ensure scalability, security, compliance and alignment with enterprise cloud architecture standards.
• Collaborate closely with data scientists, AI engineers, data engineers, platform teams and business stakeholders.

Required profile

• Minimum 5 years of experience in ML engineering, DevOps or MLOps.
• At least 3 years of recent hands‑on experience with Google Cloud Platform.
• Proven track record deploying and managing ML systems at scale.
• Bachelor’s degree in Computer Science, AI, Data Science or a 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 pipeline automation
• Docker and container orchestration concepts
• Python programming for ML workflows
• Model versioning and deployment automation
• ML monitoring, observability and reliability practices