Cloud Machine Learning Engineer

• 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 aligned to data science requirements
• Facilitate the flow of data between ML/AI models and the organisation's data systems
• Enhance data pipelines to ensure data is clean, accurate, and optimised for machine learning models
• Partner with the architecture team to evolve Big Data platform capabilities (reusable assets/patterns) and components to meet business requirements and objectives
• Research, investigate, and evaluate new technologies and methods to improve delivery and sustainability of machine learning applications and services
• Contribute to defining best practice for agile development of applications running on the Big Data platform

• Experience managing the development lifecycle for agile software development projects (Kanban or Scrum exposure)
• Robust data modelling and data architecture skills
• Knowledge of Big Data frameworks such as Hadoop, Spark, Hive, Yarn, and Airflow
• Experience with distributed ML frameworks (for example H2O and/or TensorFlow) and various ML libraries
• Proven experience creating and deploying end-to-end ML pipelines in production, including MLOps
• Programming experience in Java and Python
• Experience with containerisation (Docker/Kubernetes) or cloud alternatives is advantageous
• Mandatory working experience with AWS services including: SageMaker Pipelines, SageMaker Studio, CloudFormation, CloudTrail, SNS, EventBridge, CodePipeline, CodeBuild, CodeCommit
• Experience with other distributed technologies, NoSQL databases, and streaming technologies is desirable
• Strong written and verbal communication, with excellent interpersonal and collaboration skills
• 3-year IT or IS degree or diploma (or related field) is essential
• an advanced degree in Computer Science/Math/Statistics (or related discipline) is an advantage
• Relevant cloud certification at professional or associate level
• 5+ years of relevant experience as an AI/ML Engineer
• 5+ years of BI or related software development experience

Experience with other distributed technologies, NoSQL databases, and streaming technologies

• Opportunities to deliver business value by enabling scalable ML capabilities for local markets and group functions
• The chance to work on production-grade ML systems, end-to-end pipelines, and MLOps practices on AWS
• Collaboration with architecture teams to shape reusable Big Data platform assets and engineering patterns
• Scope to explore and evaluate new technologies and methods to improve sustainability and delivery of ML applications and services
• Strengthening best practices for productionising data science prototypes into reliable ML applications
• Deepening hands-on expertise in AWS-native MLOps and pipeline automation (including SageMaker and AWS developer tools)
• Enhancing Big Data platform design skills through reusable patterns, components, and data pipeline optimisation
• Improving cost and resource efficiency approaches across compute, network usage, and platform objectives