Machine Learning Engineer

Vor 6 Tagen

Council of the City of Sydney, Österreich Aurec Human Capital Group Vollzeit

Machine Learning & MLOps Engineers | Federal Government Location: Canberra,Melbourne,Brisbane,Hobart,Adelaide,Sydney About the Role: We are seeking experienced Machine Learning Engineers and MLOps Engineers to support a major Federal Government client. You will be instrumental in designing, deploying, and scaling sophisticated ML solutions within a secure, high-impact environment. These roles require a blend of technical excellence and the ability to navigate Australian Government security frameworks. Australian Citizen's with a current Baseline Security Clearance (or higher) are highly desirable. Machine Learning Engineer Focus: Designing and implementing high-performance models. Key Responsibilities: Design and implement ML models and algorithms for complex, large-scale datasets. Develop and maintain robust ML pipelines for training, testing, and deployment. Optimize production models for performance, scalability, and accuracy. Ensure all solutions comply with the ISM, PSPF, and ethical AI principles. Monitor model health, detecting and addressing data drift. Your Toolkit: Expertise in Python and frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of feature engineering and model optimization. Experience with cloud platforms (AWS, Azure, or GCP) and Docker/Kubernetes. MLOps Engineer Focus: Automation, CI/CD, and the ML lifecycle. Key Responsibilities: Design and maintain end-to-end ML pipelines and infrastructure. Develop automation and CI/CD workflows to transition models from dev to production. Implement monitoring and alerting for model performance and data integrity. Optimize infrastructure for cost-effectiveness and scalability. Provide technical mentorship and ensure adherence to ISM/PSPF security standards. Your Toolkit: Strong experience with MLOps tools (Kubeflow, MLflow, or Airflow). Proficiency in containerization and cloud infrastructure Background in Data Engineering and version control (Git). #J-18808-Ljbffr

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