SUMMARY:
Our client in George is looking to appoint a ML Engineer. This is a hybrid role - few days a week in the George office.
We're looking for a ML Engineer to be the dedicated engineering partner for the client's Data Science team. This is a dual-discipline role sitting at the intersection of data engineering and machine learning operations — purpose-built to ensure our Data Scientists have the data they need to build models, and that those models reliably reach production.
This is not a platform maintenance role. You'll work directly alongside three Data Scientists building feature pipelines, productionising models and establishing the engineering standards that make AI work in a regulated environment. If you're a engineer who's comfortable moving between data infrastructure and ML delivery, and who thrives when their work creates measurable business outcomes, this is your opportunity!
How this role is structured
60% Data Engineering — building and maintaining the pipelines, feature datasets and Snowflake data assets your Data Scientists need to train and validate models. 40% ML Ops — taking models from experimentation into production: deployment, versioning, monitoring and drift detection. Both disciplines serve one team: Data Science.
POSITION INFO:
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 Proven track record taking ML models from experimentation into production Strong Python skills for pipeline development, ML workflows and automation SQL proficiency for data investigation, transformation and validation Experience with Azure cloud services and infrastructure Snowflake or equivalent cloud data warehouse CI\/CD pipeline development and version control (Git) Docker and containerisation for ML and data workloads Model monitoring, drift detection and observability practices Feature engineering building datasets that reliably serve model training Awareness of data governance and regulatory requirements (POPIA, FAIS context) Strong software engineering fundamentals and production development practices Beneficial to have: Exposure to dbt for data transformation workflows Databricks Feature store design and management API development and ML model serving endpoints Insurance or financial services experience Additional: Senior enough to hold two disciplines simultaneously without dropping either under pressure A builder who takes ownership and follows work through to production Collaborative and comfortable working as the dedicated engineering partner to a Data Science team Pragmatic someone who finds the right solution for the problem, not the most complex one Naturally curious about AI, ML and emerging engineering technologies Proactive in flagging risks and unblocking teammates before issues escalate Interested in the business and commercial context behind the technology Committed to engineering quality, reliability and documentation in a regulated environment Please note only candidates that meet the minimum requirements will be considered. Please follow our website and social media channels to be the first to know when our clients have new vacancies! Instagram: LinkedIn: website:
