Department: National Disability Insurance Agency
Estimated start date: Monday, 14 December 2026
Location of work: QLD, ACT, VIC, NSW, SA
Working arrangements: Hybrid
Maximum hours: 37.5 hours per week
Security clearance: Must be able to obtain NV1
The APS6 Machine Learning Engineer (AI & Data Science) (Labour Hire) is an experienced cloud, platform and DevOps practitioner responsible for designing, automating, deploying and supporting the cloud infrastructure and engineering platforms that underpin Data Science Hub (DaSH) products and services.
Working within multidisciplinary delivery squads, the role focuses on enabling reliable development, testing, deployment and operation of machine learning, artificial intelligence, data science and software solutions. This includes supporting generative AI products, retrieval-augmented generation (RAG) systems, decision-support tools, automation solutions and analytical applications used across the Agency.
The APS6 Machine Learning Engineer exercises initiative and sound judgement when implementing cloud platforms, deployment patterns, infrastructure automation and operational capabilities. The role works closely with the EL1 Data Science & AI Engineering Lead, Lead Data Scientists, Data Scientists, AI Engineer and stakeholders to ensure solutions are scalable, secure, maintainable and operationally robust.
The position plays a key role in bridging development and operational environments, helping DaSH move solutions from proof of concept and pilot stages through to production-ready implementations while supporting Agency governance, security and reliability requirements.
Key duties and responsibilities
Responsibilities of the role include but are not limited to:
- Design, build and maintain AWS cloud infrastructure supporting AI, machine learning, software engineering and data science workloads.
- Implement and manage Infrastructure as Code solutions using technologies such as Terraform, AWS CDK or CloudFormation.
- Provision and maintain cloud environments supporting development, testing, pilots and production-ready solutions.
- Support cloud networking, IAM, storage, compute and security services required by DaSH products.
- Contribute to platform reliability, resilience, scalability and cost optimisation initiatives.
- Design, develop and maintain CI/CD pipelines to automate build, test, security scanning and deployment activities.
- Implement automated deployment processes for APIs, web applications, AI services, machine learning models and data products.
- Support release management, deployment governance and environment promotion processes.
- Contribute to software engineering best practices including Git-based source control, branching strategies, code review and automated testing.
- Continuously improve automation, reliability and developer productivity across DaSH platforms.
Containerisation & Platform Operations
- Deploy, manage and support containerised applications using Docker, Kubernetes and Amazon EKS.
- Configure and maintain application deployment environments supporting generative AI, machine learning and analytical products.
- Support application configuration, environment management and secrets management solutions.
- Assist development teams with platform, deployment and infrastructure troubleshooting.
- Maintain operational readiness of DaSH cloud platforms and services.
- Develop monitoring, logging, alerting and observability capabilities to support operational management and service reliability.
- Implement dashboards and automated reporting supporting platform health and performance monitoring.
- Contribute to incident investigation, root cause analysis and operational improvement activities.
- Monitor platform utilisation and provide recommendations for performance improvement and optimisation.
- Support reliability engineering practices that improve service availability and user experience.
Security, Governance & Compliance
- Implement security controls aligned with Agency cyber security requirements and cloud governance standards.
- Support identity and access management, secrets management and secure deployment practices.
- Assist with platform hardening, vulnerability management and remediation activities.
- Ensure solutions comply with Agency privacy, security, accessibility and governance requirements.
- Support audit activities and compliance reporting where required.
AI Platform & MLOps Enablement
- Support the deployment and operational management of machine learning, artificial intelligence and generative AI solutions.
- Contribute to MLOps and LLMOps capabilities supporting model deployment, evaluation, monitoring and lifecycle management.
- Support AI engineering environments used for experimentation, prototyping and production delivery.
- Assist with deployment approaches for retrieval-augmented generation (RAG), document intelligence and AI workflow automation solutions.
- Work closely with the EL1 Data Science & AI Engineering Lead to implement engineering standards, reusable deployment patterns and platform capabilities.
Collaboration & Delivery Support
- Collaborate with Lead Data Scientists, Software Engineers, AI Engineers, Machine Learning Engineers and Business Analysts to support solution delivery.
- Provide technical advice and implementation support during discovery, proof of concept, pilot and production activities.
- Develop operational runbooks, technical documentation and engineering standards.
- Contribute to knowledge-sharing and continuous improvement activities across DaSH.
- Support multidisciplinary teams in delivering secure, scalable and maintainable digital products.
- Demonstrated experience designing, deploying and supporting cloud-based solutions using Amazon Web Services (AWS).
- Demonstrated experience developing and maintaining CI/CD pipelines to build, test and deploy applications and services reliably.
- Experience implementing Infrastructure as Code solutions using technologies such as Terraform, AWS CDK, CloudFormation or equivalent.
- Experience working with containerisation and orchestration technologies, including Docker and Kubernetes, preferably Amazon EKS.
- Experience implementing monitoring, logging and observability solutions for cloud-based applications and platforms.
- Demonstrated ability to troubleshoot complex infrastructure, deployment and operational issues.
- Experience supporting machine learning, artificial intelligence or data science platforms.
- Experience with MLOps, LLMOps or AI model deployment and monitoring practices.
- Familiarity with Amazon Bedrock and the deployment of generative AI or retrieval-augmented generation (RAG) solutions.
- Experience implementing cloud security controls, identity and access management, secrets management and compliance automation.
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