Remote Principal ML Engineer – MLOps & CI/CD Architect

Placements24; Pretoria; Hybrid; ZAR 1,000,000 - 2,000,000; Full time

Benefits offered by this job

Fully remote work

Performance bonus

Job summary

Placements24 is seeking a Principal Machine Learning Engineer (MLOps) for a fully remote role. You will architect robust ML pipelines, deploy and monitor models, and optimize cloud-based ML infrastructure across AWS, GCP, and Azure.

You will lead CI/CD for ML, collaborate with data scientists and software engineers, and mentor junior colleagues while keeping up with evolving MLOps practices.

Qualifications

• Master's or Ph.D. in CS/SE or related quantitative field.
• 8+ years in software or ML engineering with MLOps focus.
• Experience designing end-to-end ML pipelines.
• Proficiency with cloud platforms and containerization tools.
• Experience with CI/CD tools and IaC (Terraform/Ansible).
• Strong ML concepts knowledge and ability to work remotely.

Responsibilities

• Design and maintain scalable MLOps infrastructure and workflows.
• Develop CI/CD pipelines for ML models with testing, deployment, monitoring.
• Collaborate with data scientists to operationalize models.
• Implement model monitoring, drift detection, and performance tracking.
• Manage cloud-based ML infra (AWS SageMaker, GCP, Azure ML).
• Create automation scripts to improve ML lifecycle efficiency.
• Ensure security, scalability, and cost-effectiveness of deployments.
• Mentor junior engineers and share knowledge.
• Stay updated on latest MLOps tools and best practices.

Skills

MLOps

CI/CD

Cloud platforms

Python

Docker

Kubernetes

Terraform

Ansible

Jenkins

Monitoring

Communication

Education

Master's degree

PhD

Tools

AWS

GCP

Azure

Job description

Placements24 is seeking a Principal Machine Learning Engineer (MLOps) for a fully remote role. You will architect robust ML pipelines, deploy and monitor models, and optimize cloud-based ML infrastructure across AWS, GCP, and Azure.

You will lead CI/CD for ML, collaborate with data scientists and software engineers, and mentor junior colleagues while keeping up with evolving MLOps practices.