MLOps Engineer: Cloud AI Deployment & Automation

Network Contracting; Johannesburg; On-site; ZAR 900,000 - 1,500,000; Full time

Job summary

Network Contracting seeks an experienced MLOps Engineer to design, implement, and maintain end-to-end ML deployment pipelines in a modern cloud environment. You will automate training, testing, deployment, and monitoring, while building scalable ML infrastructure and ensuring production reliability.

You will collaborate with data scientists and DevOps teams, applying CI/CD best practices and IaC to govern ML lifecycle, security, and performance across platforms.

Qualifications

• Bachelor's degree in a relevant field.
• 3+ years in MLOps, DevOps, Data Engineering, or ML Engineering.
• Strong Python and scripting experience.
• Hands-on with Azure, AWS, or Google Cloud.
• Experience with Docker and Kubernetes.
• Knowledge of CI/CD and Infrastructure as Code.
• Experience with ML lifecycle tools such as MLflow, Kubeflow, Airflow.
• Familiarity with ML production deployment processes.
• Strong troubleshooting, automation, and problem-solving skills.

Responsibilities

• Design, implement, and maintain end-to-end ML deployment pipelines.
• Automate model training, testing, deployment, and monitoring processes.
• Build and manage scalable cloud-based ML infrastructure.
• Monitor model performance and ensure reliability in production environments.
• Implement CI/CD best practices for ML workflows.
• Collaborate with Data Scientists to operationalise ML models.
• Manage model versioning, governance, and lifecycle processes.
• Ensure security, scalability, and performance of AI platforms.

Skills

Python

Scripting

Cloud platforms

Docker

Kubernetes

CI/CD

Infrastructure as Code

MLflow

Kubeflow

Airflow

Education

Bachelor's Degree in Computer Science, Engineering, Data Science, or a related field

Tools

Docker

Kubernetes

MLflow

Kubeflow

Airflow

Job description

Network Contracting seeks an experienced MLOps Engineer to design, implement, and maintain end-to-end ML deployment pipelines in a modern cloud environment. You will automate training, testing, deployment, and monitoring, while building scalable ML infrastructure and ensuring production reliability.

You will collaborate with data scientists and DevOps teams, applying CI/CD best practices and IaC to govern ML lifecycle, security, and performance across platforms.