MLOps Engineer: AI Infrastructure & Hybrid Work

Placements24; Stellenbosch Local Municipality; Hybrid; ZAR 720,000 - 1,000,000; Full time

Benefits offered by this job

Hybrid work model

Professional development opportunities

Collaborative environment

Job summary

Placements24 in Paarl, Western Cape, is seeking a proactive MLOps Engineer to streamline the ML lifecycle, deploying, monitoring and maintaining models in production. This hybrid role blends ML, software engineering and DevOps to build robust pipelines and scalable infrastructure.

You will collaborate with data scientists and engineers on cloud workloads (AWS/Azure/GCP), Docker/Kubernetes, and CI/CD tools, with a strong emphasis on data and model validation, retraining strategies, and seamless

Qualifications

• 3+ years of experience in DevOps, software engineering, or MLOps.
• Proficiency with cloud platforms (AWS, Azure, or GCP).
• Experience with containerization (Docker, Kubernetes) and CI/CD tools.

Responsibilities

• Design, build, and maintain scalable CI/CD pipelines for ML models.
• Implement infrastructure for model training, deployment, and serving.
• Monitor production model performance and plan retraining and updates.
• Automate data and model validation and production deployment.
• Collaborate with data scientists and software engineers to integrate ML models.
• Manage cloud infrastructure related to ML workloads.

Skills

DevOps experience

MLOps

Python scripting

Cloud platforms

CI/CD

Education

Bachelor's degree in Computer Science/Engineering

Tools

Docker

Kubernetes

Jenkins

GitLab CI

CircleCI

Job description

Placements24 in Paarl, Western Cape, is seeking a proactive MLOps Engineer to streamline the ML lifecycle, deploying, monitoring and maintaining models in production. This hybrid role blends ML, software engineering and DevOps to build robust pipelines and scalable infrastructure.

You will collaborate with data scientists and engineers on cloud workloads (AWS/Azure/GCP), Docker/Kubernetes, and CI/CD tools, with a strong emphasis on data and model validation, retraining strategies, and seamless