- Define, implement and manage test automation tools, frameworks and methodologies promoting an automation-first approach across all Quality Assurance activities.
- Foster and promote a QA Engineering approach, uplifting automation capabilities across the QA team as well as within projects and delivery squads.
- Define appropriate levels of test automation coverage for new initiatives as well as BAU activities, leading a team of QA Analysts in delivering maintainable and robust automated suites.
- Promote and foster a 'shift-left' approach to QA, demonstrating QA value across design and delivery of solutions.
- Estimate test automation efforts including resources, licensing and infrastructure required.
- Work closely with the DevOps practice to embed automated testing in CI/CD pipelines, enabling faster delivery cycles whilst ensuring quality of releases.
- Actively manage Test Automation tools to ensure frameworks leverage modern QA practices.
- Mentor and guide a team of QA Analysts in delivering test automation work on time and on budget.
- Leverage automation tools to generate test data, setup and validate environments.
- Be a champion for automation and agile ways-of-working, continuously identifying new automation opportunities, managing an automation backlog.
- Conduct peer reviews of development work.
- Playing an active role in establishing and maturing the RMIT QA Community of Practice.
- Assist the QA Manager for Ad Hoc testing duties.
We are looking for a skilled Machine Learning Engineer to design, develop, and deploy scalable machine learning models that solve real-world business problems. The ideal candidate will work closely with data scientists, software engineers, and product teams to build intelligent, data-driven systems.
Key Responsibilities
- Design, build, and deploy machine learning models and pipelines
- Analyze large datasets to extract insights and improve model performance
- Develop and maintain data preprocessing, feature engineering, and model evaluation workflows
- Optimize models for performance, scalability, and reliability
- Integrate ML models into production systems and APIs
- Monitor model performance and retrain models as needed
- Stay up to date with the latest machine learning techniques and tools
Required Skills & Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related field
- Strong understanding of machine learning algorithms (supervised, unsupervised, deep learning)
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn)
- Experience with data processing tools (Pandas, NumPy)
- Knowledge of SQL and data storage systems
- Understanding of model evaluation metrics and validation techniques
All your information will be kept confidential according to EEO guidelines.
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