Hybrid ML Engineer & MLOps Specialist

Placements24; Benoni; Hybride; ZAR 900 000 - 1 200 000; Plein temps

Avantages offerts par ce poste

Competitive salary

Health insurance

Hybrid work model

Professional development opportunities

Résumé du poste

Placements24 in Benoni, Gauteng seeks a skilled Machine Learning Engineer with a strong MLOps background to join their hybrid team. You will build and maintain scalable ML pipelines, deploy models to production, and ensure reliability across systems.

Collaborate with data scientists and software engineers, implement ML CI/CD, monitor model performance, and manage drift and retraining with best practices for versioning, testing, and documentation.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related quantitative field.
• 3+ years of experience in Machine Learning Engineering with a strong MLOps focus.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience with ML platforms (MLflow, Kubeflow, SageMaker, Azure ML).
• Experience with Docker and Kubernetes.

Responsabilités

• Design, build, and maintain scalable MLOps pipelines for model training, deployment, and monitoring.
• Automate ML workflows including data preprocessing, model validation, and A/B testing.
• Manage ML model serving infrastructure for high availability and low latency.
• Develop CI/CD pipelines for ML models.
• Monitor production models, detect drift, and plan retraining.
• Collaborate with data scientists to optimize models for production.
• Apply best practices for version control, testing, and documentation of ML systems.
• Troubleshoot deployment and performance issues.

Connaissances

MLOps

Python

CI/CD

ML model deployment

Collaboration

Formation

Bachelor's or Master's degree in Computer Science or Engineering

Outils

TensorFlow

PyTorch

scikit-learn

MLflow

Kubeflow

SageMaker

Azure ML

Docker

Kubernetes

AWS

GCP

Description du poste

Placements24 in Benoni, Gauteng seeks a skilled Machine Learning Engineer with a strong MLOps background to join their hybrid team. You will build and maintain scalable ML pipelines, deploy models to production, and ensure reliability across systems.

Collaborate with data scientists and software engineers, implement ML CI/CD, monitor model performance, and manage drift and retraining with best practices for versioning, testing, and documentation.