MLOps AI Software Engineer – Hybrid, Scalable ML Pipelines

Placements24; Stellenbosch; Hybrid; ZAR 900,000 - 1,400,000; Full time

Benefits offered by this job

Health insurance

Retirement contributions

Training programs and conference支持

Job summary

Placements24 is seeking an AI Software Engineer focused on MLOps to join our Stellenbosch team. You will build and maintain CI/CD pipelines, model training and deployment infrastructure, and monitoring systems to accelerate AI capabilities.

You will collaborate with data scientists and engineers in a hybrid environment, containerizing models and ensuring scalable, reliable ML workflows while staying current with industry best practices.

Qualifications

• Bachelor's degree in CS/SE or related field.
• Proven Software Engineer with MLOps exposure.
• Python proficiency and shell scripting experience.
• Experience with Docker and Kubernetes.
• Familiarity with cloud ML services (AWS/Azure/GCP).
• Experience with CI/CD tooling (Jenkins, GitLab CI, GitHub Actions).
• Understanding ML lifecycle and model deployment.

Responsibilities

• Design, implement, and manage CI/CD pipelines for ML models.
• Build and maintain infrastructure for model training, deployment, and monitoring.
• Automate ML workflows from data prep to evaluation.
• Implement model versioning, tracking, and reproducibility.
• Containerize models and prepare for production with data scientists.
• Set up monitoring to track model performance and drift.
• Ensure scalable, reliable, secure ML infrastructure.
• Troubleshoot ML deployment and performance issues.
• Stay updated on MLOps tools and practices.

Skills

Python

Shell scripting

CI/CD

Docker

Kubernetes

ML concepts

Problem solving

Hybrid teamwork

Education

Bachelor's degree in Computer Science or Software Engineering

Tools

Jenkins

GitLab CI

GitHub Actions

Job description

Placements24 is seeking an AI Software Engineer focused on MLOps to join our Stellenbosch team. You will build and maintain CI/CD pipelines, model training and deployment infrastructure, and monitoring systems to accelerate AI capabilities.

You will collaborate with data scientists and engineers in a hybrid environment, containerizing models and ensuring scalable, reliable ML workflows while staying current with industry best practices.