Machine Learning Engineer (APS6)

WattleCore Technologies Pty Ltd; Melbourne; Hybrid; AUD 70,000 - 110,000; Part time

Job summary

WattleCore Technologies Pty Ltd in Melbourne seeks an experienced APS6 Machine Learning Engineer (AI & Data Science) for a part-time 12-month contract. You will design, automate and support cloud infrastructure and engineering platforms underpinning DaSH products and services.

You will collaborate with data scientists, engineers and stakeholders to enable scalable, secure, and production-ready ML/AI solutions with strong governance. Hybrid work is available, and start is mid-December 2026.

Qualifications

• Experience designing and deploying cloud-based solutions on AWS.
• Experience developing and maintaining CI/CD pipelines.
• Experience implementing Infrastructure as Code (Terraform, AWS CDK, CloudFormation).
• Experience with Docker and Kubernetes (preferably EKS).
• Experience implementing monitoring and observability for cloud apps.
• Ability to troubleshoot complex infrastructure and deployment issues.

Responsibilities

• Cloud infrastructure: design, build and maintain AWS infrastructure for AI, machine learning, software engineering and data science workloads, with IaC.
• DevOps and CI/CD: design and maintain pipelines for APIs, apps and AI services; manage releases and environment promotion.
• Containers and platform operations: deploy and support Docker, Kubernetes and EKS; manage secrets and configurations.
• Monitoring and observability: develop dashboards, logging and alerts; participate in incident analysis and optimization.
• Security, governance and compliance: implement security controls; support IAM, secrets, audit and compliance reporting.
• AI platform and MLOps enablement: support deployment and operation of ML/AI solutions; contribute to MLOps/LMMOps capabilities.
• Collaboration and delivery: work with data scientists and engineers; produce runbooks and engineering standards.

Skills

AWS

CI/CD

Terraform

Kubernetes

Docker

Observability

Security

MLOps

LLMOps

Tools

AWS CDK

CloudFormation

Docker

Kubernetes

Amazon EKS

Terraform

AWS Bedrock

Job description

Part-time Contract Mid-level Senior

Description

About the role

The APS6 Machine Learning Engineer (AI & Data Science) 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 enables reliable development, testing, deployment and operation of machine learning, artificial intelligence, data science and software solutions — including generative AI products, retrieval-augmented generation (RAG) systems, decision-support tools, automation solutions and analytical applications used across the Agency.

You will exercise initiative and sound judgement in implementing cloud platforms, deployment patterns, infrastructure automation and operational capabilities, working closely with the EL1 Data Science & AI Engineering Lead, Lead Data Scientists, Data Scientists, the AI Engineer and stakeholders so that solutions are scalable, secure, maintainable and operationally robust. The position bridges development and operations, helping DaSH move solutions from proof of concept and pilot through to production-ready implementations within the Agency's governance, security and reliability requirements.

DaSH delivers data science, machine learning, AI and advanced analytics capabilities that support better Agency decision-making, operational efficiency and participant outcomes. Its work is experimental and iterative, spanning discovery, proof of concept, pilot and production readiness, and calls for engineering that is flexible enough for prototyping yet disciplined enough to meet governance, assurance, privacy, security and operational requirements.

The role can be based at the NDIA's offices in Brisbane, Surry Hills (Sydney), Deakin (Canberra), Richmond (Melbourne) or Geelong, or Adelaide. Estimated start 14 December 2026. 12-month initial contract with one 12-month extension option. 37.5 hours per week. Hybrid working is available in line with current NDIA policy, subject to negotiation with the line manager.

You must be able to obtain a Negative Vetting Level 1 security clearance.

Australian citizenship is mandatory. The successful candidate must provide evidence of citizenship during the pre-engagement check.

Responsibilities

• Cloud infrastructure: design, build and maintain AWS infrastructure for AI, machine learning, software engineering and data science workloads, with Infrastructure as Code in Terraform, AWS CDK or CloudFormation, covering networking, IAM, storage, compute and security services, and contributing to reliability, resilience, scalability and cost optimisation.
• DevOps and CI/CD: design and maintain pipelines that automate build, test, security scanning and deployment for APIs, web applications, AI services, machine learning models and data products; support release management, deployment governance and environment promotion; and uphold Git-based source control, branching, code review and automated testing practices.
• Containers and platform operations: deploy, manage and support containerised applications on Docker, Kubernetes and Amazon EKS; maintain deployment environments, configuration and secrets management for generative AI, machine learning and analytical products; and help development teams troubleshoot platform, deployment and infrastructure issues.
• Monitoring and observability: develop monitoring, logging, alerting and observability capabilities, dashboards and automated reporting; contribute to incident investigation, root cause analysis and operational improvement; and recommend performance and utilisation optimisations.
• Security, governance and compliance: implement security controls aligned with Agency cyber security and cloud governance standards; support identity and access management, secrets management and secure deployment; assist with platform hardening, vulnerability management and remediation; and support audit and compliance reporting.
• AI platform and MLOps enablement: support the deployment and operation of machine learning, AI and generative AI solutions; contribute to MLOps and LLMOps capabilities for model deployment, evaluation, monitoring and lifecycle management; and assist with deployment approaches for RAG, document intelligence and AI workflow automation, implementing engineering standards and reusable patterns with the EL1 lead.
• Collaboration and delivery: work with Lead Data Scientists, Software Engineers, AI Engineers, Machine Learning Engineers and Business Analysts through discovery, proof of concept, pilot and production; and develop operational runbooks, technical documentation and engineering standards.

Skills & experience

• Demonstrated experience designing, deploying and supporting cloud-based solutions on 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 with Terraform, AWS CDK, CloudFormation or equivalent.
• Experience 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.

Nice to have

• 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.