MLOps Engineer: Build & Scale AI Production Pipelines

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

Benefits offered by this job

Hybrid work model

Performance bonuses

Health insurance

Learning & certification

Job summary

Placements24 is seeking a skilled MLOps Engineer to enhance AI & Emerging Technologies infrastructure in Mahikeng. The role focuses on the ML lifecycle from development to deployment, monitoring, and maintenance in a hybrid setup.

You will build and manage robust pipelines, collaborate with data scientists and software engineers, and ensure scalability, reliability, and security across production ML systems.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 3+ years in DevOps, MLOps, or Software Engineering with ML focus.
• Proficiency in Python, Bash and CI/CD tooling.
• Experience with cloud platforms (AWS, Azure, GCP) and containers (Docker, Kubernetes).
• Strong understanding of ML lifecycles and workflows.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Responsibilities

• Develop, automate, and maintain CI/CD pipelines for ML models.
• Manage infrastructure for training, deploying, and monitoring ML models.
• Ensure scalability, reliability, and performance of ML systems in production.
• Collaborate with data scientists and software engineers to operationalize ML workflows.
• Monitor model performance, drift, and retraining strategies.
• Manage MLOps tools to ensure security and compliance.

Skills

Python

CI/CD

Bash

Cloud platforms

ML workflows

ML frameworks

Education

Bachelor's or Master's in CS/Engineering

Tools

Docker

Kubernetes

TensorFlow

PyTorch

scikit-learn

Job description

Placements24 is seeking a skilled MLOps Engineer to enhance AI & Emerging Technologies infrastructure in Mahikeng. The role focuses on the ML lifecycle from development to deployment, monitoring, and maintenance in a hybrid setup.

You will build and manage robust pipelines, collaborate with data scientists and software engineers, and ensure scalability, reliability, and security across production ML systems.