ML Engineer

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 enjoymentwe 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 engineeringexpertiseto design and operationalise scalableproduction-grademachine learningsystems across the bank. This includes architecting robust data and compute infrastructureestablishingstrongMLOpsfoundations and enablinghigh performancedeployments on Azure Databricks AKS Spark AirflowandMLflow. The role advances the banks ML capabilities and provides technical leadershipenterprise 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