Senior MLOps Engineer

Project overview

We are currently seeking an experienced Senior MLOps Engineer to support a major Federal Government organisation in developing and operationalising enterprise-scale artificial intelligence and machine learning capabilities. This is an opportunity to work on complex, high-impact technology initiatives, building the platforms, automation and processes that enable machine learning models to be securely deployed, monitored and maintained in production environments.

Your duties will include:

Skills and Experience we are looking for:

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Strong programming and data engineering skills using Python, SQL and Spark/PySpark, including experience developing batch and streaming data pipelines.

Experience working with on-premises machine learning infrastructure is particularly relevant, with potential opportunities to support future transitions to AWS or Microsoft Azure cloud environments.

Application Process: If you would like to apply, please contact Vamshi on email [contact removed]

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• Contract: Initial contract of up to 12 months, with extensions.
• Location: Brisbane, Melbourne or Sydney. Initial 6 weeks onsite, with hybrid working arrangements potentially available thereafter.
• Security Clearance: Must have the ability to obtain a Baseline security clearance.
• Key Technologies: Python, SQL, Spark/PySpark, Kubernetes, Red Hat OpenShift AI, CI/CD, MLflow/Kubeflow and enterprise data platforms.
• Design, develop and maintain automated MLOps pipelines supporting machine learning model development, testing, deployment, monitoring and retraining.
• Build and optimise batch and real-time data pipelines to support enterprise machine learning and analytics workloads.
• Implement and manage containerised machine learning environments using Kubernetes, preferably Red Hat OpenShift AI.
• Collaborate with data scientists, software engineers and business stakeholders to transition machine learning models into reliable production solutions.
• Establish automated testing, release management and deployment processes, ensuring appropriate security, governance and rollback capabilities.
• Implement model monitoring, experiment tracking and version control to improve the reliability, performance and maintainability of machine learning solutions.
• Support responsible AI practices, including privacy, security, compliance and ongoing improvements to MLOps engineering standards.
• Demonstrated experience in AI/ML engineering, including hands-on experience deploying and supporting machine learning solutions in production environments.
• Practical experience with MLOps frameworks such as MLflow, Kubeflow or equivalent technologies for experiment tracking, model versioning and monitoring.
• Experience with Kubernetes, containerisation and Linux, ideally including Red Hat OpenShift AI and managing compute resources for machine learning workloads.
• Strong understanding of CI/CD, Git, infrastructure automation and DevSecOps, including automated testing, deployment orchestration and secure release management.
• Experience working with enterprise data platforms, relational databases, distributed storage and data lake technologies such as Hadoop, Hive, Impala and Kafka. Experience with Cloudera Data Platform would be highly regarded.
• Demonstrated ability to lead technical delivery, troubleshoot complex production issues and communicate effectively with multidisciplinary teams. Relevant tertiary qualifications in computer science, mathematics, statistics or a related discipline will be highly regarded.

Project details

• Start: 1 December 2026
• Duration: 12 months
• Remote: hybrid
• Location: Sydney, Australia, AU