Risk Analytics Engineer – ML Ops & Production

Standard Bank Group; Johannesburg; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Standard Bank Group seeks a Risk Analytics Engineer to operationalize risk models in production and deliver robust MLOps pipelines within a cross-functional squad. You will containerize and deploy models as secure, versioned APIs, monitor data drift and model health, and collaborate with data scientists, data engineers and platform engineers to ensure scalable, low-latency decisioning.

The role emphasizes automation, software engineering best practices, and strong Python development in a

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related quantitative field.
• 3–5+ years in DevOps, Machine Learning Operations, Software Engineering or Data Engineering with automation.
• Strong programming proficiency, particularly in Python; proven experience with CI/CD tools (GitHub Actions, Azure DevOps, Jenkins).

Responsibilities

• Operationalize risk models in production environments.
• Design, build, and maintain automated CI/CD pipelines to test, validate, and deploy risk models and decisioning logic.
• Package and deploy models as secure, versioned APIs.
• Implement production monitoring for data drift and model health.
• Collaborate with Data Scientists, Data Engineers and Platform Engineers to ensure seamless production deployment.

Skills

Python

CI/CD tooling

GitHub Actions

Azure DevOps

Jenkins

Docker

Kubernetes

AWS

Azure

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

Terraform

CloudFormation

AWS

Azure

Job description

Standard Bank Group seeks a Risk Analytics Engineer to operationalize risk models in production and deliver robust MLOps pipelines within a cross-functional squad. You will containerize and deploy models as secure, versioned APIs, monitor data drift and model health, and collaborate with data scientists, data engineers and platform engineers to ensure scalable, low-latency decisioning.

The role emphasizes automation, software engineering best practices, and strong Python development in a