Key Responsibilities
Data Engineering
Design and build scalable feature pipelines and training datasets for machine learning models.
Develop and maintain high-quality data assets within Snowflake.
Build reliable, monitored and well-documented data pipelines for model training and inference.
Collaborate with Data Engineering teams to align with platform standards and architecture.
Validate data quality and ensure consistency with business definitions.
Apply data governance principles and regulatory requirements including POPIA, FAIS and TCF.
Machine Learning Operations (MLOps)
Partner with Data Scientists to productionise machine learning models.
Build and maintain deployment pipelines and model serving infrastructure.
Implement CI/CD processes for machine learning workflows.
Manage model versioning, experiment tracking and reproducible deployments.
Monitor models for performance, reliability and data drift.
Maintain documentation, auditability and model lineage.
Support responsible AI practices, including explainability and model governance.
Troubleshoot production issues and continuously improve model performance.
Engineering & Collaboration
Help establish ML Engineering standards and best practices.
Contribute to the architecture of our AI ecosystem across Azure and GCP.
Work closely with Analytics Engineers to integrate machine learning into business solutions.
Identify opportunities to improve automation, tooling and delivery.
Proactively identify risks and recommend practical solutions.
Qualifications
Bachelor's degree in Computer Science, Data Science, Software Engineering, Information Technology, Mathematics, Statistics or a related quantitative field.
A postgraduate qualification in Artificial Intelligence, Machine Learning or Data Science will be advantageous.
Relevant industry certifications in Azure, Google Cloud, Snowflake or Machine Learning are advantageous.
Skills & Experience
Essential
5+ years' experience in Machine Learning Engineering, Data Engineering or a similar role.
Proven experience deploying machine learning models into production.
Strong Python development skills.
Advanced SQL skills.
Experience with Snowflake or another cloud data warehouse.
Experience with Azure cloud services.
Knowledge of Git, CI/CD pipelines and modern software engineering practices.
Experience with Docker and containerisation.
Experience building feature engineering pipelines.
Understanding of model monitoring, observability and drift detection.
Knowledge of data governance and regulatory frameworks such as POPIA and FAIS.
Advantageous
Experience with dbt.
Databricks experience.
Feature Store implementation and management.
API development and model serving.
Experience within insurance or financial services.
Exposure to GCP environments.