Machine Learning Engineer – Emirati Talent (NAFIS)
نظرة عامة على الوظيفة
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تاريخ الإعلانأبريل 13, 2026
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الموقع
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تاريخ إنتهاء الصلاحيةيونيو 18, 2026
المسمى الوظيفي
411_3367966
Position MLOPs Engineer I Section Data & AI Division e& enterprise.
المسؤوليات
- Build and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
- Automate model workflows using CI/CD and Continuous Training (CT) principles.
- Develop and manage reusable pipeline templates for LLM fine-tuning, RAG deployments, and multi-agent orchestration.
- Optimize cloud resource usage for efficient large-model training and inference
- Lead ML model lifecycle automation including CI/CD pipelines, monitoring, and production deployments
- Establish MLOps best practices for model versioning, automated testing, and continuous deployment
- Manage inference endpoints for real-time and batch predictions across environments (Dev, Staging, Prod).
- Implement model monitoring for drift detection, performance degradation, and latency tracking
- Manage feature stores, data versioning, and lineage tracking for reproducibility.
- Collaborate with AI engineers and data scientists to optimize model training and deployment workflows
- Drive adoption of DevOps practices in ML teams including infrastructure as code and automated deployments
- Design security and compliance frameworks for ML infrastructure and model governance
- Manage cost optimization and resource allocation for ML training and inference workloads
- Work closely with AI Architects, ML Engineers, and DevOps to improve pipeline reliability.
Qualifications & Experience
- Emirati with Family book
- Male candidates must have completed the National service.
- Bachelor’s or Engineering degree in Computer Science, Data Science, or related technical field.
- 2+ years of experience in DevOps, platform engineering, or ML infrastructure
- Knowledge of Kubernetes, Docker, and cloud platforms (AWS, GCP, Azure)
- Strong experience with Infrastructure as Code tools (Terraform, CloudFormation) and CI/CD pipelines; MLOps tools knowledge like Airflow, Kubeflow, MLflow etc.
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2026-04-08 15:37:25