MACHINE LEARNING ENGINEER - Solve the Hard Problems of Production ML in FinTech - CAPE TOWN (Hybrid), up to R1.4m

This is an excellent opportunity for a MACHINE LEARNING ENGINEER who wants to work on the engineering challenges that start once a model leaves the notebook.

You’ll join one of South Africa’s leading FinTech businesses and help build the systems that allow Machine Learning to operate reliably in production - at scale.

Based in CAPE TOWN, hybrid (2 days per week in office) - this MACHINE LEARNING ENGINEER role offers up to R1.4m.

THE COMPANY:

This is a highly ambitious FinTech business using technology, data and Machine Learning to improve access to fast, intelligent financial services for SMEs.

Technology sits at the heart of the business, and they are now investing further in the infrastructure needed to deploy, operate and scale ML reliably in production.

For an ambitious ML Engineer, this is a chance to join while that capability is still evolving - and to have real input into how production ML is built.

THE ROLE:

Your challenge is not simply to build models. It’s to help make them work in the real world.

You’ll work across Machine Learning, Software Engineering, Data Engineering and DevOps, helping move models from experimentation into reliable production services.

You’ll get involved in areas such as:

• Productionising Machine Learning models
• Model serving and inference
• Python APIs and backend services
• Real-time and event-driven pipelines
• Docker and Kubernetes
• Cloud infrastructure
• CI/CD and automated deployment
• Infrastructure as Code
• Monitoring and observability
• MLOps tooling
• Large-scale data processing

You’ll work closely with experienced Data Scientists and Engineers and gradually take greater ownership of how ML systems are designed, deployed and improved.

THE TECH:

The environment includes:

• Python, SQL, Kafka, Docker, Kubernetes, Terraform, Spark, MLflow, CI/CD and GCP/AWS/Azure.

You do not need experience with everything on that list.

What matters more is a strong engineering foundation and some meaningful exposure to Machine Learning in production.

REQUIRED SKILLS:

Strong Python and good Software Engineering fundamentals

Experience building production-quality software

Some exposure to deploying or supporting ML systems

Experience with cloud, containers, APIs or data pipelines

A good understanding of testing, automation and maintainable code

The ability to learn quickly and solve difficult engineering problems

Experience with Kubernetes, Kafka, Terraform, MLflow, Spark or MLOps tooling would be valuable, but is not essential.

You may currently be a Machine Learning Engineer, MLOps Engineer, Software Engineer working with ML, or Data Engineer moving towards ML Engineering.

You do not need to tick every box.

What matters is that you are technically strong, curious, and excited by the challenge of making Machine Learning reliable, scalable and useful in the real world.

Experience in FinTech, banking, lending, credit or financial services would be useful, but is not required.