CLOUD MACHINE LEARNING ENGINEER (AWS)

Who We Are

About This Role

What You’ll Do

• Design and develop machine learning systems and implementation patterns.
• Automate predictive model software, including model training.
• Productionise data science prototypes and develop machine learning applications.
• Facilitate data flow between ML/AI models and organisational data systems.
• Enhance data pipelines for clean, accurate, and machine-learning-optimised data.
• Partner with architecture teams on reusable Big Data assets, patterns, and components.
• Research technologies and methods that improve ML delivery and sustainability.
• Contribute to agile development best practices for applications on the Big Data platform.

Experience and Core Requirements

• Experience managing agile software development lifecycles, including Kanban or Scrum exposure.
• Strong data modelling and data architecture skills.
• Knowledge of Big Data frameworks including Hadoop, Spark, Hive, Yarn, and Airflow.
• Experience with distributed ML frameworks such as H2O or TensorFlow and other ML libraries.
• Production experience creating and deploying end-to-end ML pipelines, including MLOps.
• Programming experience in Java and Python.
• Experience with Docker, Kubernetes, or cloud alternatives is advantageous.
• Experience with other distributed technologies, NoSQL databases, and streaming technologies is desirable.
• Strong written and verbal communication, interpersonal, and collaboration skills.

Qualifications

• A three-year IT, Information Systems, or related degree or diploma is essential.
• An advanced degree in Computer Science, Mathematics, Statistics, or a related discipline is an advantage.
• Relevant cloud certification at professional or associate level is required.
• At least five years of relevant AI/ML engineering experience and five years of BI or related software development experience are required.

What’s In It For You

• Opportunities to enable scalable ML capabilities for local markets and group functions.
• Work on production-grade ML systems, end-to-end pipelines, and AWS MLOps practices.
• Collaborate with architecture teams on reusable Big Data platform assets and engineering patterns.
• Explore technologies and methods that improve ML application delivery and sustainability.

What Skills You Will Learn

• Strengthen practices for productionising data science prototypes into reliable ML applications.
• Deepen AWS-native MLOps and pipeline automation expertise, including SageMaker and AWS developer tools.
• Develop Big Data platform design skills through reusable patterns, components, and pipeline optimisation.
• Improve cost and resource efficiency across compute, network usage, and platform objectives.