ML Engineer - George

Description

Data Engineering for AI & Data Science You'll build and own the data infrastructure that powers the Data Science team ensuring they always have clean, reliable, well-structured data to work with.

• Design and build feature pipelines and training datasets that support model development and validation
• Build and maintain high-quality data assets in Snowflake that serve AI and analytical workloads
• Collaborate with Data Engineering to align on platform standards without absorbing core modernisation backlog
• Develop scalable feature engineering capabilities and contribute to feature management best practices
• Ensure data pipelines supporting model training and inference are reliable, monitored and well-documented
• Apply awareness of data governance and regulatory obligations (POPIA, FAIS, TCF) when building and managing data assets used in AI systems
• Validate data quality and ensure model inputs align with agreed business definitions

ML Ops & Model Productionisation You'll close the gap between data science experimentation and production ensuring models built by the team reach the business reliably and at scale.

• Partner with Data Scientists to productionise machine learning models and AI solutions • Design, build and maintain ML deployment pipelines and model serving infrastructure • Implement CI/CD practices for machine learning workflows and automated model delivery • Manage model versioning, experiment tracking and reproducible deployments • Monitor deployed models for performance, data drift, reliability and operational health • Ensure model outputs, data lineage and deployment decisions are documented and auditable • Contribute to responsible AI practices explainability, monitoring and model risk controls • Troubleshoot production issues and continuously improve model and pipeline performance Engineering Standards & Collaboration You will help establish the engineering rigour that makes AI work trustworthy and sustainable across the Data Science team and the broader Data & AI function.

• Help establish ML engineering standards and best practices for the Data Science team • Contribute to the architecture of our growing AI ecosystem across Azure and GCP environments • Work with Analytics Engineers to integrate ML outputs into analytical and operational data products • Identify opportunities to improve automation, tooling and delivery velocity across the AI workstream • Proactively flag data, model or infrastructure risks before they become production issues Requirements:

• 5+ years' experience in ML Engineering, Data Engineering or a combined role

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