We are looking for an experienced MLOps Engineer (GCP) to design, operationalize, deploy, monitor, and scale production-grade AI/ML solutions on Google Cloud Platform (GCP). In this role, you will build reliable, secure, and automated end-to-end machine learning platforms and pipelines while enabling seamless collaboration between Data Scientists, AI Engineers, Platform Teams, and Operations teams. You will play a key role in ensuring machine learning models are consistently trained, versioned, deployed, monitored, and governed across their lifecycle using GCP-native technologies, particularly Vertex AI. This role is based onsite in Jeddah, KSA. Applicants must be willing and ready to relocate to Jeddah, Saudi Arabia.
Key Responsibilities Design and implement scalable end-to-end MLOps architectures using GCP-native services. Build standardized frameworks for model training, deployment, monitoring, retraining, and governance. Deploy and manage ML models using Vertex AI Endpoints for online and batch inference. Implement model versioning, rollout/rollback strategies, and traffic splitting for production deployments. Build and automate CI/CD pipelines for ML workflows and model deployment. Develop automated ML pipelines using Vertex AI Pipelines and ensure reproducibility across environments (development, testing, and production). Integrate source control, testing frameworks, and artifact repositories into ML workflows. Monitor model performance, model drift, data quality, and system reliability. Implement observability, logging, alerting mechanisms, and service-level objectives (SLOs) for ML systems. 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.
Requirements Experience5+ years of experience in ML Engineering, Dev Ops, MLOps, or related engineering roles. Minimum 3+ years of recent hands-on experience with Google Cloud Platform (GCP) (mandatory). Strong production experience deploying and managing ML systems at scale.
Technical Skills Strong hands-on experience with Google Cloud Platform (GCP). Deep expertise with Vertex AI including Pipelines, Endpoints, Model Registry, and Monitoring. Strong understanding of CI/CD practices, infrastructure automation, and ML lifecycle management. Experience with Docker and containerization/orchestration concepts. Strong Python programming skills for ML workflows and automation. Experience with ML monitoring, observability, reliability, and scalability practices. Knowledge of model versioning, deployment automation, and production operations.
Education & Certifications Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. GCP certifications such as Professional Cloud Dev Ops Engineer or equivalent are a strong plus.
Preferred Candidate Profile Strong problem-solving mindset with a focus on automation and reliability. Experience working in cross-functional AI/ML environments. Ability to work in production-grade cloud environments and drive operational excellence for ML systems. Strong communication and stakeholder collaboration skills. Fluent English, Arabic is a plus