Machine Learning Operations (MLOps) Engineer

Placements24; Centurion; Hybrid; ZAR 600,000 - 900,000; Full time

Benefits offered by this job

Hybrid work model

Competitive salary

Professional development

Job summary

Placements24 in Centurion is seeking a skilled MLOps Engineer to bridge ML model development and production deployment. You will design and maintain robust pipelines for continuous integration, delivery, and monitoring of ML models.

You will collaborate with data scientists and engineers to ensure scalable, reliable ML deployments with automated infrastructure in a hybrid work setup in Centurion. A strong software engineering background and passion for automation are essential.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of software engineering or DevOps experience with MLOps.
• Proficiency with Python or Bash and containerization.
• Experience with cloud ML services (AWS/Azure/GCP).
• Familiarity with ML frameworks and CI/CD.

Responsibilities

• Develop, automate, and maintain the end-to-end MLOps lifecycle, from data ingestion to model deployment and monitoring.
• Implement CI/CD pipelines for machine learning models, ensuring smooth and efficient model updates.
• Build and manage infrastructure for training and serving ML models, leveraging cloud services.
• Monitor model performance in production, identify drift, and implement retraining strategies.
• Collaborate with data scientists and software engineers to streamline development and deployment.
• Ensure reliability, scalability, and security of ML systems in production environments.

Skills

Python

Bash scripting

CI/CD

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

AWS

TensorFlow

PyTorch

scikit-learn

Job description

About the Role

Our client is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their innovative technology team in Centurion . This role is crucial for bridging the gap between machine learning model development and production deployment. You will be responsible for building and maintaining robust pipelines for continuous integration, delivery, and monitoring of ML models. The ideal candidate will have a strong background in software engineering principles, a deep understanding of ML workflows, and a passion for automation and operational excellence. You will work closely with data scientists and engineers to ensure ML models are deployed efficiently, reliably, and at scale, contributing directly to the operational success of our client's AI initiatives.

Key Responsibilities

• Develop, automate, and maintain the end-to-end MLOps lifecycle, from data ingestion to model deployment and monitoring.
• Implement CI/CD pipelines for machine learning models, ensuring smooth and efficient model updates.
• Build and manage infrastructure for training and serving ML models, leveraging cloud services.
• Monitor model performance in production, identify drift, and implement strategies for retraining and redeployment.
• Collaborate with data scientists and software engineers to streamline model development and deployment processes.
• Ensure the reliability, scalability, and security of ML systems in production environments.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering or DevOps, with a focus on MLOps.
• Proficiency in scripting languages (e.g., Python, Bash) and containerization technologies (Docker, Kubernetes).
• Experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) and model deployment strategies.
• Strong understanding of CI/CD principles and infrastructure as code tools.

Benefits

• Competitive salary and performance bonuses.
• Comprehensive health insurance and retirement savings plan.
• Generous opportunities for professional development and learning new technologies.
• Flexible hybrid working arrangements.
• Exposure to cutting-edge MLOps practices and tools.
• A dynamic and supportive team environment focused on innovation.