Machine Learning Engineer

About the role

As a Machine Learning Engineer in the AI Solutions team of Sanlam Life & Savings, you will drive the operationalisation of AI and ML models across the organisation. You will bridge cutting‑edge DevOps practices with machine‑learning engineering to deliver scalable, reliable, and secure AI solutions that support data‑driven decision making.

Key responsibilities

• Design, implement and maintain production‑grade ML/AI pipelines using CI/CD best practices.
• Lead the integration of DevOps practices within the ML lifecycle, including version control, containerisation, orchestration and monitoring.
• Deploy and optimise machine‑learning models in cloud‑native environments such as AWS, Azure and GCP.
• Implement MLOps best practices for model versioning, validation, retraining and monitoring.
• Innovate DevOps frameworks and automation strategies to keep the organisation at the forefront of AI operational excellence.
• Collaborate with business development teams and technology SMEs to translate requirements into robust, scalable AI solutions.
• Perform root‑cause analysis and resolve issues across infrastructure and application layers.
• Provide architectural guidance for AI/ML solution design and deployment.
• Mentor junior engineers and promote a culture of continuous learning and technical excellence.

Required profile

• Strong experience with DevOps tools and practices, including CI/CD pipelines, GitOps and infrastructure‑as‑code solutions.
• Proficiency in containerisation and orchestration technologies such as Docker and Kubernetes.
• Hands‑on experience deploying workloads on major cloud platforms (AWS, Azure, GCP).
• Demonstrated ability to implement MLOps processes and monitor production models.
• Excellent problem‑solving skills and the ability to work collaboratively with cross‑functional teams.

Required skills

• CI/CD pipelines
• GitOps
• Terraform
• CloudFormation
• Pulumi
• Docker
• Kubernetes
• Amazon ECS
• Amazon EKS
• Google Kubernetes Engine
• AWS
• Azure
• GCP
• MLOps