Remote MLOps Engineer: CI/CD Pipelines & Cloud Infra

Freudenberg Nonwovens; Cape Town; Remote; ZAR 600,000 - 1,200,000; Full time

Benefits offered by this job

Remote-friendly salary

Fully remote with flexible hours

Health benefits

Paid time off

Learning & development

Job summary

Freudenberg Nonwovens is seeking a Remote Machine Learning Operations Engineer (MLOps) to join our distributed technology team. You will build and maintain the ML infrastructure across experimentation to production, ensuring reliability at scale.

Work remotely with data scientists and software engineers to streamline deployment and operationalization of ML models in cloud environments, implementing robust CI/CD pipelines and scalable ML workflows.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred.
• 3–5 years in MLOps, DevOps, or SRE with ML focus.
• Proficiency in Python and scripting; cloud platforms experience.
• Strong containerization knowledge with Docker and Kubernetes.
• Experience with ML frameworks and data pipelines; CI/CD familiarity.

Responsibilities

• Design, build, and maintain robust CI/CD pipelines for ML models.
• Manage infrastructure for training, deployment, and serving on cloud platforms.
• Develop monitoring for ML performance, drift, and health.
• Collaborate with data scientists to optimize production-ready models.
• Implement version control for data, code, and models.
• Troubleshoot ML system performance and reliability issues.

Skills

MLOps experience

DevOps experience

Python proficiency

Scripting languages

CI/CD knowledge

Data pipelines

Monitoring tools

Cloud platforms (AWS/GCP/Azure)

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Scikit-learn

Job description

Freudenberg Nonwovens is seeking a Remote Machine Learning Operations Engineer (MLOps) to join our distributed technology team. You will build and maintain the ML infrastructure across experimentation to production, ensuring reliability at scale.

Work remotely with data scientists and software engineers to streamline deployment and operationalization of ML models in cloud environments, implementing robust CI/CD pipelines and scalable ML workflows.