MLOps Engineer

Recruiter:

Network Contracting

Job Ref:

NCM003411/TS

Date posted:

Monday, September 7, 2026

Location:

Johannesburg, South Africa

Salary:

Monthly

SUMMARY:
Passionate about bridging the gap between Machine Learning and Production? Join a technology-driven organisation where you'll play a key role in deploying, automating, and scaling AI solutions that deliver real business value.

POSITION INFO:
Our client is an innovative organisation committed to leveraging data and AI to drive business transformation. They are seeking an experienced MLOps Engineer to build and maintain robust machine learning infrastructure, ensuring models are deployed efficiently, monitored effectively, and continuously improved. Working closely with Data Scientists, Data Engineers, and DevOps teams, you will help bring AI solutions from experimentation to production. What's in It for You? Work on cutting-edge AI and Machine Learning initiatives. Collaborate with highly skilled technology professionals. Exposure to modern cloud, automation, and MLOps technologies. Opportunities for career growth and continuous learning. Competitive salary and benefits package. Dynamic and innovation-focused work environment. Key Responsibilities Design, implement, and maintain end-to-end ML deployment pipelines. Automate model training, testing, deployment, and monitoring processes. Build and manage scalable cloud-based ML infrastructure. Monitor model performance and ensure reliability in production environments. Implement CI\/CD best practices for machine learning workflows. Collaborate with Data Scientists to operationalise machine learning models. Manage model versioning, governance, and lifecycle processes. Ensure security, scalability, and performance of AI platforms. Experience & Skills of the Ideal Candidate Bachelor's Degree in Computer Science, Engineering, Data Science, or a related field. 3+ years of experience in MLOps, DevOps, Data Engineering, or Machine Learning Engineering. Strong experience with Python and scripting languages. Hands-on experience with cloud platforms such as Azure, AWS, or Google Cloud. Experience with containerisation technologies such as Docker and Kubernetes. Knowledge of CI\/CD pipelines and Infrastructure as Code. Experience with ML lifecycle tools such as MLflow, Kubeflow, Airflow, or similar. Familiarity with machine learning frameworks and production deployment processes. Strong troubleshooting, automation, and problem-solving skills. Ready to build the infrastructure that powers next-generation AI solutions? Apply today and be part of a team shaping the future of Machine Learning. .

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