ML Engineer | Hybrid at Badger Holdings (Pty) Ltd

About the Role

• As our Machine Learning Engineer, you'll operate at the intersection of Data Engineering and Machine Learning Operations (MLOps), turning experimentation into production-ready AI solutions.
• Approximately 60% of your role will focus on building reliable data pipelines, feature datasets and Snowflake assets that power model development.
• The remaining 40% will focus on MLOps, including deploying, monitoring and maintaining machine learning models in production while establishing engineering best practices.

Requirements

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.