Hybrid MLOps Engineer - Deploy & Scale ML Systems

Placements24; Benoni; Hybrid; ZAR 700,000 - 1,100,000; Full time

Benefits offered by this job

Competitive salary

Hybrid work model

Access to advanced MLOps technologies

Career growth opportunities

Job summary

Placements24 is seeking a skilled MLOps Engineer to join our Benoni team. You will design and maintain CI/CD pipelines, build scalable infrastructure for training, deployment, and monitoring of ML models, and collaborate with data scientists and software engineers to operationalize models.

You will work in a hybrid environment, ensuring reliability, scalability, and secure ML operations across the production lifecycle in South Africa.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or MLOps.
• Proficiency with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Experience with ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow).
• Strong understanding of automation and infrastructure as code (IaC).
• Excellent problem-solving and collaboration skills.

Responsibilities

• Design, implement, and manage CI/CD pipelines for machine learning models.
• Develop and maintain infrastructure for model training, deployment, and monitoring.
• Implement strategies for model versioning, experimentation tracking, and reproducibility.
• Collaborate with data scientists and software engineers to operationalize ML models.
• Monitor model performance in production and troubleshoot issues.
• Ensure the scalability, security, and reliability of the MLOps platform.

Skills

CI/CD

Cloud platforms

IaC

Collaboration

Problem-solving

Automation

Education

Bachelor's degree in Computer Science/Engineering

Tools

Docker

Kubernetes

MLflow

Kubeflow

Job description

Placements24 is seeking a skilled MLOps Engineer to join our Benoni team. You will design and maintain CI/CD pipelines, build scalable infrastructure for training, deployment, and monitoring of ML models, and collaborate with data scientists and software engineers to operationalize models.

You will work in a hybrid environment, ensuring reliability, scalability, and secure ML operations across the production lifecycle in South Africa.