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Full-time
On-site
LinkedIn
Posted 1 month, 1 week ago
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Job Description
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**
. A
**pplicants 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**
*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