Machine Learning Engineer

Bellville, South Africa | Posted on 08/07/2026

Location: Cape Town |
Work Type: Hybrid |
Job : J107221

Our client, a reputable financial services firm listed on the Johannesburg Stock Exchange (JSE), believes in the transformative power of incremental progress. They empower individuals and businesses by offering tailored solutions and expert guidance to help pave the way to success. With a focus on innovation and excellence, they provide secure financial futures through personalised advice and cutting-edge products to support diverse goals and aspirations.

• Design, train, and evaluate machine learning models (predictive, deep learning, or NLP/GenAI depending on project needs) to solve complex business problems.
• Lead the development of scalable, robust, and automated end-to-end ML pipelines (data ingestion, feature engineering, training, and deployment).
• Contribute to the architecture of ML infrastructure, ensuring models are containerized, deployed efficiently, and easily integrated into core software products.
• Implement continuous monitoring strategies to track model performance, data drift, and latency, driving iterative improvements post-deployment.
• Conduct experimentation to evaluate new algorithms, open-source frameworks, and methodology advancements.
• Partner with Data Scientists, Software Engineers, and Product Managers to translate business requirements into technical solutions.

• A relevant tertiary qualification would be beneficial (Computer Science, Data Science, Machine Learning, etc.).
• 3 - 5+ years of professional experience developing, scaling, and deploying ML models in a production environment.
• Strong proficiency in Python and ecosystem libraries (e.g., PyTorch, Tensor Flow, scikit-learn, Pandas).
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., Docker, Kubernetes, MLflow, or cloud-native equivalents like Sage Maker/Vertex AI).
• Solid understanding of statistical modelling, feature engineering, and data manipulation skills (including strong SQL).
• Familiarity with software engineering best practices, including Git version control, CI/CD pipelines, and writing clean, testable code.
• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.