AI Engineer

stude; City of Brisbane; On-site; AUD 120,000 - 160,000; Full time

Job summary

stude seeks an experienced AI/LLM engineer to design, develop, and maintain production AI systems, including LLM applications and RAG solutions, within a security-conscious framework.

You will build and operate AWS-based infrastructure (ECS/Fargate, Lambda, S3) and data pipelines, collaborating with the CIO and cross-functional teams to turn business challenges into scalable, compliant technology solutions. The role emphasizes ownership, continuous learning, and clear communication.

Qualifications

• Production AI systems experience with LLM apps, agentic workflows or RAG.
• Hands-on design and operation of AWS cloud-native infra.
• Strong Python; TypeScript/Node.js also valued.
• Infrastructure-as-code is second nature.
• Architect scalable, secure systems with robust data pipelines.
• Security, privacy and governance mindset applied to data.
• Autonomous, self-directed delivery of outcomes.
• Clear written and verbal communication.

Responsibilities

• Design, develop, and maintain AI-powered solutions and LLM apps.
• Build scalable cloud infra and data pipelines in AWS.
• Deliver end-to-end solutions from requirements to deployment and support.
• Collaborate with CIO and stakeholders to translate challenges into solutions.
• Stay current with AI tech and share knowledge across the team.

Skills

AI/LLM Engineering

Cloud Infrastructure

Software Engineering

Infrastructure-as-code

System Design

Security Mindset

Autonomy & Ownership

Communication

Tools

ECS/Fargate

Lambda

S3

API Gateway

IAM

Job description

Design, develop, and maintain AI-powered solutions, including LLM applications, automation tools, intelligent document processing and agentic workflows. Build and support scalable cloud infrastructure, data pipelines and integrations within AWS to enable reliable AI and business applications. Deliver end-to-end solutions, from requirements gathering and solution design through to development, deployment, optimisation and ongoing support. Develop and integrate advanced AI capabilities, including RAG solutions, prompt engineering, model orchestration and API-driven applications. Ensure all solutions align with security, privacy, governance and compliance requirements, particularly when handling sensitive customer data. Collaborate closely with the CIO and cross-functional stakeholders to translate business challenges into practical high impact technology solutions. Stay current with emerging AI technologies and contribute to continuous improvement, knowledge sharing and engineering best practices across the team.

What You’ll Bring

• AI/LLM Engineering (3–5 years). Demonstrated experience building production AI systems - LLM applications, agentic workflows, RAG, NLP, intelligent document processing, or similar. Not just prototypes - shipped, maintained, production systems.
• Cloud Infrastructure (AWS). Hands-on experience designing and operating cloud-native infrastructure - ECS/Fargate, Lambda, S3, API Gateway, IAM, networking. At least 2 years in AWS.
• Software Engineering. Strong Python skills (TypeScript/Node.js also valued). Clean code, version control, CI/CD, testing and production debugging.
• Infrastructure-as-code is second nature.
• System Design. Ability to architect scalable secure systems - API design, event-driven patterns, data pipelines and integration architecture. You think about failure modes, not just happy paths.
• Security Mindset. You've built systems handling sensitive data. Authentication, authorisation, encryption and audit controls are part of how you think - not an afterthought.
• Autonomy & Ownership. You take ambiguous problems and deliver complete solutions without hand-holding. Self-directed, proactive and accountable for outcomes.
• Communication. Able to explain technical work clearly to both technical and non-technical audiences. Written and verbal.

Qualifications

No formal degree requirement - equivalent experience and demonstrated capability will be considered equally. AWS certifications, such as Solutions Architect, Developer or Machine Learning Specialty, are highly regarded. Relevant AI/ML certifications or completion of advanced coursework are desirable. Demonstrated commitment to continuous learning and professional development within AI and machine learning, including publications, open-source contributions, personal projects or similar initiatives.