Machine Learning Operations (MLOps) Engineer (GCP)
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Key skills for this role
About the Role
TMC Middle East seeks a Consultant MLOps Engineer (GCP) to own production ML systems on GCP, including CI/CD, monitoring, and incident response. Requires 5+ years ML Engineering/MLOps/DevOps and 3+ years GCP experience.
Key Skills for This Role
Responsibilities
- Design, build, and run end to end MLOps architecture on GCP
- Implement CI/CD automation for ML from Git commit to canary deployment on Vertex AI Endpoints
- Set up production monitoring including drift detection, alerting, and automated retraining triggers
- Handle incident response for model degradation in production
Requirements
- 5+ years ML Engineering / MLOps / DevOps
- 3+ years GCP mandatory
- Deployed a production ML system at scale
- Active GCP code within the last 12 18 months
- Deep knowledge of Vertex AI (pipelines, model registry, endpoints, monitoring)
- Production incident story with real root cause and fix
Full Job Posting
Role Overview
- TMC Middle East is hiring a Consultant MLOps Engineer (GCP) on behalf of a leading organization in Saudi Arabia.
- This is a production ownership role: operationalize, monitor, and scale ML systems with real traffic and SLOs.
What you'll actually own
- End to end MLOps architecture on GCP. You design it, you build it, you run it.
- CI/CD automation for ML — from Git commit to canary deployment on Vertex AI Endpoints. Every step automated, every step auditable.
- Production monitoring — drift detection, alerting, automated retraining triggers. You know before the business does when something is wrong.
- Incident response. When a model degrades in production, you're the person with the answer — root cause, rollback, post mortem, prevention.
What we need to see — non negotiable
- You deployed a production ML system at scale. Tell us the QPS, the latency SLO, and one failure mode you personally resolved.
- You wrote GCP code within the last 12–18 months. Active. Recent. Not theoretical.
- You know Vertex AI from the inside — pipelines, model registry, endpoints, monitoring. War stories preferred.
- You have a production incident story. Real root cause. Real fix. Real post mortem action.
- You can whiteboard your last GCP ML architecture cold — no notes, no prep.
- 5+ years ML Engineering / MLOps / DevOps. 3+ years GCP mandatory. GCP Professional Cloud DevOps Engineer cert is a strong plus.
What we don't want
- "I've worked with Vertex AI" — with no endpoint, no monitoring config, no numbers
- POC builders who've never carried a production SLO
- Anyone whose GCP muscle is 2+ years stale
Additional Details
- Location: Saudi Arabia
- Seniority: Consultant / Senior
- Stack: GCP · Vertex AI · Kubeflow · Cloud Build · Docker · Python · Terraform
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