Remote MLOps Engineer: Scale AI Pipelines

Placements24; Vereeniging; Hybrid; ZAR 720,000 - 900,000; Full time

Job summary

Placements24 is seeking a highly motivated Machine Learning Operations (MLOps) Engineer to join its technology team. The role is primarily remote with potential Vereeniging site engagement, focusing on operationalizing AI, building scalable ML infrastructure and CI/CD pipelines.

You will collaborate with data scientists and software engineers to automate training, testing, deployment and monitoring of ML models, applying IaC and best practices throughout the ML lifecycle.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Proven MLOps/DevOps experience with automation and infrastructure management.
• Proficiency with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes).
• Experience with scripting languages such as Python and familiarity with ML frameworks.
• Strong understanding of software development best practices and CI/CD principles applied to ML lifecycles.

Responsibilities

• Design, build, and maintain CI/CD pipelines for machine learning models.
• Implement infrastructure as code (IaC) for ML environments using tools like Terraform or Ansible.
• Develop and manage systems for model monitoring, versioning, and performance tracking.
• Collaborate with data scientists and software engineers to ensure smooth deployment and operation of ML models.
• Automate ML workflows, from data preparation to model retraining and deployment in Vereeniging.

Skills

MLOps

DevOps

CI/CD

Python

Cloud knowledge

Automation

Education

Bachelor's degree in Computer Science/Engineering

Tools

AWS

Azure

GCP

Docker

Kubernetes

Terraform

Ansible

Python

Job description

Placements24 is seeking a highly motivated Machine Learning Operations (MLOps) Engineer to join its technology team. The role is primarily remote with potential Vereeniging site engagement, focusing on operationalizing AI, building scalable ML infrastructure and CI/CD pipelines.

You will collaborate with data scientists and software engineers to automate training, testing, deployment and monitoring of ML models, applying IaC and best practices throughout the ML lifecycle.