Senior MLOps Engineer - Scalable AI Deployments (Hybrid)

Placements24; Richards Bay; Hybrid; ZAR 1,000,000 - 1,600,000; Full time

Benefits offered by this job

Medical aid

Retirement fund contributions

Hybrid work model

Job summary

Placements24 is seeking a Senior Machine Learning Engineer (MLOps) to extend its AI capabilities in Richards Bay, KwaZulu-Natal. You will bridge model development and production, designing robust pipelines and infrastructure for scalable deployments.

The role emphasizes cloud-native ML, CI/CD for ML, and collaboration with data scientists to accelerate iteration. Ideal candidates have 4+ years in ML engineering with MLOps, strong Python skills, and hands-on experience with Kubeflow, MLflow, TFX

Qualifications

• Bachelor's or Master's in CS/Engineering or related field.
• 4+ years in Machine Learning Engineering with MLOps focus.
• Strong Python and ML libraries experience.
• Experience with MLOps tools and cloud platforms.
• Proficient in CI/CD for ML, data pipelines, and distributed computing.

Responsibilities

• Design and maintain scalable MLOps pipelines for training, validation, deployment and monitoring.
• Develop infrastructure for ML workloads on cloud and container platforms.
• Implement CI/CD practices for ML models.
• Collaborate with data scientists to streamline model lifecycle.
• Automate data validation, feature pipelines, and retraining processes.
• Monitor models for drift and performance, with corrective actions.

Skills

Python

CI/CD

Problem solving

Collaboration

ML deployment

Teamwork

Education

Bachelor's in CS or related field

Master's preferred

Tools

Kubeflow

MLflow

TFX

Seldon Core

AWS

Azure

GCP

Docker

Kubernetes

Python libraries (scikit-learn, TensorFlow, PyTorch)

Job description

Placements24 is seeking a Senior Machine Learning Engineer (MLOps) to extend its AI capabilities in Richards Bay, KwaZulu-Natal. You will bridge model development and production, designing robust pipelines and infrastructure for scalable deployments.

The role emphasizes cloud-native ML, CI/CD for ML, and collaboration with data scientists to accelerate iteration. Ideal candidates have 4+ years in ML engineering with MLOps, strong Python skills, and hands-on experience with Kubeflow, MLflow, TFX