* Machine Learning Engineers
* 6 months initial contract with possible extension
* Adelaide Based Position
Our client is seeking a highly skilled Machine Learning Engineer to lead the deployment, automation, and optimisation of machine learning models in preproduction and production environments. This role is critical in enabling scalable, secure, and efficient ML workflows that support data-driven initiatives.
Duties and Responsibilities:
* Design, implement, and maintain ML pipelines for model training, deployment, and monitoring.
* Develop automation and CI/CD workflows for ML models using tools such as
* Ensure compliance with Australian Government security frameworks (ISM, PSPF) and agency policies.
* Optimise ML infrastructure for performance, scalability, and cost-effectiveness.
* Collaborate with data professionals to transition models from development to production.
* Implement monitoring and alerting for model performance and data drift.
* Provide technical advice and mentorship to team members and stakeholders.
* Stay current with emerging ML Ops technologies and best practices.
Qualifications:
* Demonstrated experience in ML Ops or related roles within complex environments.
* Strong knowledge of ML lifecycle management and deployment strategies.
* Proficiency in cloud platforms (AWS, Azure, GCP) and containerisation (Docker, Kubernetes).
* Experience with ML Ops tools (Kubeflow, MLflow, Airflow) and CI/CD pipelines.
* Solid understanding of data engineering, version control (Git), and automation frameworks.
* Excellent problem-solving skills and ability to work under pressure.
Apply now or reach to Ivan Aureus at for a confidential chat