TM Machine learning r

• 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

Additional Information

All your information will be kept confidential according to EEO guidelines.