Intermediate​/SENIOR Machine Learning Engineer Hybrid; Fintech R1.– R1

If you’re a MACHINE LEARNING ENGINEER who wants to get closer to production ML, MLOps and scalable cloud infrastructure, this is a brilliant opportunity to do exactly that.

You do NOT need to tick every box.

I’m looking for an Intermediate or Senior ML Engineer with strong Python and Machine Learning foundations who has started taking models beyond experimentation and into real production environments.

One of South Africa’s most exciting Fin Tech businesses, using technology, data and Machine Learning to make funding and financial services faster and more accessible for SMEs.

Technology isn't a support function here. It sits at the heart of the business and its products.

The company is now investing further in its ML capability and the infrastructure required to deploy, operate and scale Machine Learning reliably in production.

You'll work at the intersection of Machine Learning, Software Engineering, Data Engineering and Dev Ops.

The challenge is not simply to build good models. It's making them work reliably in the real world.

You'll help:

• Take ML models from experimentation into production
• Build and improve ML pipelines and infrastructure
• Work with real-time and event-driven data
• Deploy and serve models in cloud environments
• Improve monitoring, observability and model reliability
• Build scalable, containerised ML services
• Automate deployment through CI/CD and MLOps
• Work with large datasets and distributed processing
• Make engineering decisions around performance, latency and scalability

You’ll work closely with experienced Data Scientists and Engineers, giving you the opportunity to deepen your ML Engineering skills while having genuine input into how the company's production ML capability evolves.

The essentials:

• Some commercial experience taking ML models into production
• Good Software Engineering principles
• Experience with at least one major cloud platform - AWS, GCP or Azure
• An interest in solving production ML and infrastructure problems

Experience with some of the following would be a major advantage, but I don't expect you to have everything:

Docker, Kubernetes, Terraform, Kafka, Pub/Sub, Spark, PySpark, Dataflow, Flink, MLflow, Kubeflow, CI/CD, model serving, monitoring and observability.

Whether you're already operating at Senior level or you're a strong Intermediate ML Engineer looking for the environment that will take you there, I would like to hear from you.

Fin Tech experience is useful but absolutely not essential.

If you enjoy building ML that actually gets used in production, this is the opportunity.