MLOps Engineer (GCP)

Overview

MLOps Engineer (GCP)

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

• *Experience*
• 5+ years of experience in ML Engineering, DevOps, 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 DevOps 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