Job Opportunities API

• 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.