Remote Senior ML Engineer — MLOps & Scalable AI

Placements24; Gauteng; Remote; ZAR 1,000,000 - 1,800,000; Full time

Benefits offered by this job

Remote work setup allowance

Flexible working hours

Professional development budget

Health insurance

Job summary

Placements24 is seeking a Remote Senior Machine Learning Engineer with a strong MLOps focus to join a fully distributed team. You will design and maintain scalable ML infrastructure, automate CI/CD/CT pipelines, and monitor production models to ensure reliability at scale.

You will collaborate with data scientists to optimize models for deployment, build tooling for the ML lifecycle, and stay ahead of emerging MLOps trends while enabling fast, robust AI delivery.

Qualifications

• Master's or PhD in Computer Science, Software Engineering, or related field.
• 5+ years in software engineering with at least 3 years in ML engineering/MLOps.
• Strong Python programming with ML libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with CI/CD tools (Jenkins, GitLab CI, GitHub Actions).
• Experience with containerization (Docker, Kubernetes) and cloud ML services.

Responsibilities

• Design, build, and maintain scalable ML infrastructure and pipelines.
• Implement CI/CD/CT for ML models.
• Automate deployment, monitoring and management of ML models in production.
• Collaborate with data scientists to optimize models for production.
• Develop tools/frameworks to support the ML lifecycle.
• Ensure performance, scalability and stability of ML systems.
• Troubleshoot ML deployment issues and implement monitoring.

Skills

Python

ML lifecycle

Education

Master's or PhD in CS/SE

Tools

Docker

Kubernetes

Terraform

Ansible

Prometheus

Grafana

Jenkins

Job description

Placements24 is seeking a Remote Senior Machine Learning Engineer with a strong MLOps focus to join a fully distributed team. You will design and maintain scalable ML infrastructure, automate CI/CD/CT pipelines, and monitor production models to ensure reliability at scale.

You will collaborate with data scientists to optimize models for deployment, build tooling for the ML lifecycle, and stay ahead of emerging MLOps trends while enabling fast, robust AI delivery.