Machine Learning Operations MLOps Engineer GCP

Anyone can say they've "deployed ML models on GCP." Very few can tell you the QPS, the p99 latency, and what went wrong at 2 a.m.

TMC Middle East is hiring a Consultant MLOps Engineer (GCP) on behalf of a leading organization in Saudi Arabia.

This is not a pipeline-builder role; this is a production ownership role. You will operationalize, monitor, and scale ML systems that carry real traffic, real SLOs, and real consequences when they fail.

• Design, build, and run end-to-end MLOps architecture on GCP.
• Automate CI/CD for ML, from Git commit to canary deployment on Vertex AI Endpoints, ensuring every step is automated and auditable.
• Implement production monitoring, including drift detection, alerting, and automated retraining triggers, and identify issues before business impact.
• Lead incident response: root cause analysis, rollback, post mortem, and prevention when a model degrades in production.

• Deployed a production ML system at scale; provide details on QPS, latency SLO, and a failure mode you resolved.
• Written GCP code within the last 12-18 months; recent, practical experience.
• Deep knowledge of Vertex AI (pipelines, model registry, endpoints, monitoring); war stories preferred.
• Demonstrated production incident story with real root cause, fix, and post mortem actions.
• Ability to whiteboard your last GCP ML architecture from scratch, without notes.
• 5+ years of ML Engineering / MLOps / Dev Ops experience, with 3+ years on GCP. GCP Professional Cloud Dev Ops Engineer certification is a strong plus.

• Claims of working with Vertex AI without describing endpoints, monitoring configuration, or quantitative results.
• Proof of concept builders who have never managed a production SLO.
• Candidates whose GCP experience is more than two years old.

What is the most painful Vertex AI limitation you've encountered in production, and how did you work around it? If you have an answer ready, we want to hear from you.