SENIOR Machine Learning Engineer – Build Production ML at Fintech Scale Hybrid), R

This is an excellent opportunity for a SENIOR MACHINE LEARNING ENGINEER with strong Python, production ML, MLOps and cloud engineering experience to take Machine Learning out of the notebook and make it work at scale within one of South Africa’s leading Fin Tech businesses.

Based in CAPE TOWN – hybrid, 2 days per week in office – this Senior ML Engineer role offers a salary of R1.35m – R1.45m.

This is one of South Africa’s most exciting and ambitious Fin Tech businesses, using technology and data to tackle one of the biggest challenges facing SMEs: access to fast, intelligent and frictionless financial services.

This isn't a traditional financial services company trying to become a technology business. Technology, data and intelligent decision-making sit at the heart of the company and its products.

They have built a sophisticated digital platform helping SMEs access funding and financial services faster and more simply than traditional approaches allow.

They are now investing further in their Data and ML capability and the infrastructure required to deploy, operate and scale Machine Learning reliably in production.

For a strong ML Engineer, this creates a particularly interesting opportunity: you'll join at a point where you can genuinely influence how production ML systems are architected, deployed and scaled, rather than arriving once all the important engineering decisions have already been made.

As SENIOR MACHINE LEARNING ENGINEER, your challenge isn't simply to build better models.

It's to make Machine Learning work in the real world.

How do you take a model from experimentation into production?

How do you feed it reliable data in real time?

How do you design for throughput and latency?

How do you deploy changes safely?

How do you make ML systems observable, scalable and reliable?

And how do you build an ML platform that Data Scientists and Engineers can confidently build upon?

You’ll help answer those questions.

Working at the intersection of Machine Learning, Software Engineering, Data Engineering and Dev Ops, you’ll work closely with Data Scientists and Engineers to build and evolve the infrastructure that allows ML systems to operate reliably at scale.

You’ll help design production ML systems with performance, reliability, scalability and efficiency built in from the outset – while understanding the engineering trade-offs required to achieve them.

You’ll work across:

• Real-time and event-driven data and ML pipelines
• High-throughput and low-latency system design
• Cloud-native ML infrastructure
• Containerised ML workloads
• Infrastructure as Code
• Automated deployment and CI/CD/CT
• Model serving and inference
• Monitoring and observability
• MLOps tooling and infrastructure

And importantly, you’ll help determine how these systems should be designed, rather than simply being handed a specification and told to build it.

You absolutely do not need every technology on that list.

What matters more is that you've solved the underlying engineering problems: taking ML into production, designing reliable distributed systems, working with infrastructure and building software that performs at scale.

You will need strong experience product ionising Machine Learning systems – rather than only training models or working in notebooks.

You’ll also have experience across several of the following:

• Advanced Python and good working knowledge of SQL.
• Building robust, production-quality software around Machine Learning.
• Training Machine Learning models, or working closely with Data Scientists who do.
• Real-time, event-driven or distributed systems, using technologies such as Kafka, Kafka Connect or Pub/Sub.
• Containerised and cloud-native environments using Docker and Kubernetes.
• Infrastructure as Code, ideally Terraform.
• Designing systems around throughput, latency, performance and reliability.
• Big Data technologies such as Spark, Dataflow or Flink.
• Building or contributing to CI/CD/CT and MLOps pipelines.
• ML tooling such as MLflow, Kubeflow or DVC.
• Deploying production systems into GCP, AWS or Azure.
• Applying strong Software Engineering principles to ML, including testing, automation and maintainability.

Experience with in Fin Tech, lending, banking, credit, risk or financial services would be highly advantageous, but isn't essential. A genuine interest in applying Machine Learning to complex financial problems is just as important.