Location Cape Town / Johannesburg (Hybrid)  Job Intro We are passionate about building scalable, reliable machine learning systems that create real customer impact. Our team blends technical excellence with enjoyment—we learn continuously, solve meaningful problems, and celebrate the solutions we deliver. With several production ML systems already live and more coming, this is a place to grow, contribute, and thrive. Job Purpose Apply deep ML engineering expertise to design and operationalise scalable, production-grade machine learning systems across the bank. This includes architecting robust data and compute infrastructure, establishing strong MLOps foundations, and enabling high performance deployments on Azure, Databricks, AKS, Spark, Airflow and MLflow. The role advances the bank’s ML capabilities and provides technical leadership enterprise wide.  Job Responsibilities
* Demonstrate proven cloud experience on Azure with strong system/application architecture skills (including AKS, Databricks, Spark, Airflow, and MLflow expertise), alongside expert-level knowledge of data structures, algorithms, computability and complexity, and computer architecture, with practical experience using an enterprise feature store. 
* Apply strong data science literacy - including understanding of model types, feature engineering, statistical principles, evaluation metrics, and common modelling workflows - to effectively bridge the gap between model development and production.
* Expert proficiency in programming tools (such as Python, R, etc.) for data manipulation, statistical analysis, model implementation, and production grade machine learning tasks is essential. 
* Implement MLOps practices to streamline the deployment, monitoring, and management of machine learning models in production, ensuring reproducibility, scalability and model governance.
* Develop, maintain, and evolve a scalable, reliable machine learning platform that meets community and stakeholder needs, proactively resolving performance bottlenecks and optimizing resource usage across compute and storage layers.
* Automate the end-to-end machine learning pipeline, from data ingestion, feature engineering to model deployment, monitoring and lifecycle management.
* Design and build robust inference systems, such as APIs, batch processing, and real-time streaming solutions, to facilitate the deployment and utilization of machine learning models. 
* Leverage GPU acceleration to enhance the performance and efficiency of machine learning models, particularly for deep learning tasks.  Qualification
* STEM Qualification
* Engineering, Computer Science, Econometrics, Mathematical Statistics, Actuary Science
* Masters or Doctorate will be an added advantage Minimum Experience Level
* 3-7 years’ experience in a data science or cloud based role
* Portfolio of delivering projects successfully into production