MLOps Engineer — Build Scalable AI Pipelines (Hybrid)

Placements24; Sandton; Hybrid; ZAR 900,000 - 1,300,000; Full time

Benefits offered by this job

Health insurance

Retirement contributions

Generous paid time off

Professional development

Job summary

Placements24 in Sandton, Gauteng, seeks an experienced MLOps Engineer to streamline ML deployment, monitoring, and maintenance in production. The role combines AI operations with collaboration across data science and software teams in a hybrid work setting.

The candidate should have a strong background in MLOps, DevOps, and cloud ML services, with proficiency in Python, Docker, Kubernetes, and CI/CD tooling. A CS/Engineering degree is preferred for this position.

Qualifications

• Experience in MLOps, DevOps, or ML systems.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Proficiency in Python scripting and programming.
• Experience with Docker and Kubernetes for ML deployments.
• Familiarity with CI/CD tools (Jenkins, GitLab CI, GitHub Actions).
• Knowledge of infrastructure as code (Terraform, CloudFormation).
• Strong problem-solving and collaboration skills.

Responsibilities

• Design, implement, and manage MLOps pipelines for training, deployment, and monitoring.
• Develop infrastructure for deploying and serving ML models at scale.
• Apply CI/CD practices to ML models across environments.
• Monitor model performance, drift, and retraining needs.
• Collaborate with data scientists and engineers to optimize production ML workflows.
• Automate ML infrastructure including containers and cloud resources.
• Manage model versioning, artifacts, and experiment tracking.
• Ensure security and reliability of ML production systems.

Skills

MLOps

DevOps

Python

Docker

Kubernetes

CI/CD

Cloud platforms

Education

CS/Engineering degree

Tools

Docker

Kubernetes

Terraform

CloudFormation

Jenkins

GitLab CI

GitHub Actions

AWS

Azure

GCP

Job description

Placements24 in Sandton, Gauteng, seeks an experienced MLOps Engineer to streamline ML deployment, monitoring, and maintenance in production. The role combines AI operations with collaboration across data science and software teams in a hybrid work setting.

The candidate should have a strong background in MLOps, DevOps, and cloud ML services, with proficiency in Python, Docker, Kubernetes, and CI/CD tooling. A CS/Engineering degree is preferred for this position.