Remote MLOps Engineer: CI/CD for ML Pipelines

Placements24; LegKraal Gate; Hybrid; ZAR 600,000 - 1,000,000; Full time

Benefits offered by this job

Fully remote work

Flexible hours

Health benefits

Professional development

Job summary

Placements24 is seeking a highly skilled Remote Machine Learning Operations Engineer (MLOps) to join its distributed technology team. You will build and maintain infrastructure and pipelines supporting the ML lifecycle, from experimentation to production deployment and monitoring.

As part of a remote team, you will collaborate with data scientists and software engineers to streamline deployment and operations of ML models across cloud environments.

Qualifications

• Bachelor's degree in Computer Science or related field; master's preferred.
• 3-5 years in MLOps/DevOps with ML focus.
• Proficiency in Python and scripting; cloud experience (AWS/Azure/GCP).
• Experience with containers and orchestration (Docker, Kubernetes).
• Familiarity with ML frameworks (TensorFlow, PyTorch).
• Knowledge of data pipelines and CI/CD principles.

Responsibilities

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

Skills

Python

Scripting

CI/CD

Cloud platforms

Data pipelines

Monitoring

Education

Bachelor's degree in Computer Science

Master's preferred

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Scikit-learn

Job description

Placements24 is seeking a highly skilled Remote Machine Learning Operations Engineer (MLOps) to join its distributed technology team. You will build and maintain infrastructure and pipelines supporting the ML lifecycle, from experimentation to production deployment and monitoring.

As part of a remote team, you will collaborate with data scientists and software engineers to streamline deployment and operations of ML models across cloud environments.