ML Engineer (George)

Job description

Machine Learning Engineer

Data & AI | ARC | George, Western Cape (Hybrid negotiable) | Permanent

Build the engineering that brings AI to life.

At ARC, we're building the next generation of data, analytics and AI capabilities for Badger SA, including dotsure.co.za and Pacific International Insurance. Our purpose is simple: use intelligent technology to create Soft Landings, making work simpler, decisions smarter and businesses stronger.

We're looking for an experienced Machine Learning Engineer who enjoys solving real business problems through engineering excellence. You'll become the dedicated engineering partner to our Data Science team, ensuring they have reliable, high-quality data to build models and the infrastructure needed to deploy those models into production with confidence.

This is an opportunity to play a key role in shaping how AI is delivered across a growing insurance group, working with modern cloud technologies and influencing the future of our machine learning platform.

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.

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 and 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 and 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.

Why join ARC?

This is more than another machine learning role.

You'll have the opportunity to:

• Shape the future of AI within a growing insurance group.
• Build production-ready AI solutions that create measurable business value.
• Work with modern technologies including Python, Azure, Snowflake and dbt.
• Influence our ML Engineering standards and platform architecture.
• Collaborate with experienced data scientists and engineering specialists.
• Continue learning in a fast-growing data and AI environment where innovation is encouraged.

Working arrangement

This role is based in George, Western Cape.

We believe collaboration happens best together, so this position is primarily office-based. Hybrid working arrangements may be considered for the right candidate. Applicants who currently live in, or are willing to relocate to, George are encouraged to apply.

About ARC

ARC exists to accelerate sustainable growth through intelligent technologies, delivering soft landings for businesses, people and communities.

We combine business expertise, data engineering, analytics, automation and AI to help organisations make smarter decisions, move faster and grow with confidence.

Ready to build the future of AI?

If you're excited by solving complex engineering challenges, enabling data scientists to do their best work, and building AI solutions that make a real business impact, we'd love to hear from you.