Secure AI Engineer (Specialist)

CyberCX; Canberra; On-site; AUD 140,000 - 180,000; Full time

Job summary

CyberCX in Canberra is seeking a Secure AI Engineer to deliver consulting and engineering services across Secure AI, AI-enabled applications, and modern cloud environments. You will work with clients and teammates across design reviews, implementation support, and governance to help organisations adopt AI securely.

The role involves reviewing AI-enabled workflows, identifying risks across models, prompts, data flows and APIs, and building technical controls and artefacts.

Qualifications

• Experience in software engineering, ML engineering, data science, cloud engineering or security engineering.
• Experience building or supporting modern applications or cloud-based systems.
• Familiarity with secure development practices and security concepts across AI and cloud.
• Ability to investigate issues, structure findings and communicate clearly.
• Exposure to AI, ML or Generative AI use cases.
• Experience with AWS, Azure, or Google Cloud.
• Familiarity with CI/CD, containers, or Kubernetes.

Responsibilities

• Support the delivery of secure AI and application security engagements for enterprise and government clients.
• Contribute to reviews of AI-enabled applications, workflows, platforms, and integrations.
• Assist in identifying risks across models, prompts, data flows, APIs, cloud services, and supporting systems.
• Help define practical recommendations to improve security, governance, and delivery controls.
• Build or configure technical controls, patterns, and supporting artefacts for secure AI implementations.
• Produce clear, high-quality client deliverables, findings, recommendations, implementation guidance, and supporting documentation.
• Work with client stakeholders to understand their environment, priorities, and constraints.
• Collaborate across CyberCX practices where engagements span cloud, identity, application security, data, or governance domains.

Skills

Software engineering

ML engineering

Data science

Cloud engineering

Security engineering

Technical consulting

Tools

AWS

Azure

Google Cloud

CI/CD

Infrastructure as Code

Kubernetes

Job description

The Secure AI Engineer is responsible for the delivery of consulting and engineering services across Secure AI, AI-enabled applications, and modern cloud environments.

Working closely with clients, teammates, and other practices you will contribute to a range of engagements across design review, implementation support, control uplift, and technical assessment, helping organisations adopt AI in a secure and responsible way.

What You'll Do

• Support the delivery of secure AI and application security engagements for enterprise and government clients
• Contribute to reviews of AI-enabled applications, workflows, platforms, and integrations
• Assist in identifying risks across models, prompts, data flows, APIs, cloud services, and supporting systems
• Help define practical recommendations to improve security, governance, and delivery controls
• Build or configure technical controls, patterns, and supporting artefacts for secure AI implementations
• Produce clear, high-quality client deliverables, including technical products, findings, recommendations, implementation guidance, and supporting documentation
• Work with client stakeholders to understand their environment, priorities, and constraints
• Contribute to reusable templates, reference patterns, and internal capability uplift across the Secure AI team
• Collaborate across CyberCX practices where engagements span cloud, identity, application security, data, or governance domains

What You'll Bring

• Experience in software engineering, ML engineering, data science, cloud engineering, security engineering, or technical consulting
• Experience building, reviewing, or supporting modern applications or cloud-based systems
• Familiarity with secure development practices and common security concepts across AI, applications, infrastructure, and integrations
• Ability to investigate technical issues, structure findings, and communicate clearly
• Exposure to AI, ML, Generative AI, Agentic AI, or AI-assisted development use cases
• Experience with AWS, Azure, or Google Cloud
• Familiarity with application security review, threat modelling, IAM, API security, or workload security
• Exposure to CI/CD, infrastructure as code, containerised workloads, or Kubernetes
• Awareness of common security or risk frameworks such as NIST, ISO 27001, CIS, MAESTRO, or similar