ML Engineer | Hybrid
Badger Holdings Pty Ltd · George, Western Cape
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Start free — we apply for you →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.
About You
You'll thrive in this role if you are
- A collaborative engineer who enjoys partnering with Data Scientists.
- Passionate about building reliable, scalable machine learning solutions.
- Comfortable balancing data engineering with production ML engineering.
- Curious about emerging AI technologies and best practices.
- Pragmatic, solutions-focused and commercially aware.
- Someone who takes ownership and follows work through to completion.
- Committed to engineering quality, documentation and continuous improvement.