MLOps Engineer قالب وصف وظيفي (يونيو 2026) (تنزيل مجاني)

What does an MLOps Engineer do?

The MLOps Engineer is responsible for building and maintaining the infrastructure, pipelines, and tooling that enable machine learning models to be developed, deployed, monitored, and retrained reliably at scale. This role bridges the gap between data science and production engineering.

Free MLOps Engineer Job Description Template

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What are the Key Responsibilities of MLOps Engineer

Design and maintain CI/CD pipelines for machine learning model training and deployment.
Build and manage feature stores, model registries, and experiment tracking systems.
Monitor deployed models for performance degradation, data drift, and concept drift.
Automate model retraining and redeployment workflows.
Collaborate with data scientists to containerize and productionize ML models.
Ensure reproducibility, versioning, and governance of ML experiments and artifacts.

What are the Skills and Requirements for an MLOps Engineer?

Experience with MLOps platforms such as MLflow, Kubeflow, or SageMaker.
Strong proficiency in Python and infrastructure-as-code tools (Terraform, Helm).
Familiarity with containerization technologies including Docker and Kubernetes.
Understanding of data pipeline orchestration tools such as Airflow or Prefect.

What are the KPIs to track for MLOps Engineer?

Performance is evaluated based on model deployment frequency, pipeline reliability, reduction in model degradation incidents, and infrastructure cost efficiency.

Deployment Frequency

Number of successful model deployments per sprint or month.

Model Uptime

Availability and reliability of models in production environments.

Drift Detection Rate

Speed and accuracy of identifying data or model drift before impact.

تقارير إلى

Head of ML Engineering / VP of Engineering

يتعاون مع

Data Scientists, ML Engineers, Data Engineers, DevOps Engineers

يؤدي

Are any specific tools or software required for the MLOps Engineer role?

MLflow
Kubeflow
Docker
Kubernetes
Airflow
Terraform

What is the qualification of MLOps Engineer?

Bachelor's degree in Computer Science, Engineering, or related field; 3+ years of experience in ML engineering or DevOps with hands-on MLOps platform experience.

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