Agentic AI Engineer

The Agentic AI Engineer will design, build and deploy production-grade AI solutions that improve productivity, decision-making and operational performance across the company’s Industries. You will also focus on large language models, agentic workflows, retrieval-augmented generation, business-system integrations and intelligent automation.

Company Profile:

Our client is a wholly owned Australian company manufacturing a large range of steel products including Garden Sheds and Outdoor Buildings. From its manufacturing facility in Brisbane, our client distributes an extensive range of outdoor storage products throughout Australia, as well as exporting to the Pacific region and Europe. They have been a major supplier of outdoor storage products for over 55 years. The good reputation they have built is now being recognized throughout Australia and rapidly expanding to overseas markets including New Zealand and Europe.

This is an exciting opportunity to join a growing organisation that is investing in AI, e-commerce, and digital transformation. As an Agentic AI Engineer, you will work closely with technology and business teams to design, build, and deploy production-ready AI solutions, including LLM applications, RAG systems, and intelligent agent workflows. The role offers the opportunity to work on practical AI use cases that improve productivity, automate processes, connect business systems, and deliver measurable operational outcomes.

Duties and Responsibilities:

• Design, build and deploy LLM-powered applications and agentic AI workflows.

• Develop AI agents capable of using approved tools and APIs, completing multi-step tasks and escalating decisions for human approval where required.

• Build RAG solutions across structured and unstructured business information.

• Develop document-ingestion, chunking, embedding, metadata, retrieval, reranking and response-grounding pipelines.

• Integrate AI solutions with ERP, inventory, manufacturing, sales, finance, supply-chain and customer-service systems.

• Build secure and maintainable APIs connecting AI applications with business systems and databases.

• Move solutions from proof of concept through testing, deployment, monitoring and ongoing production support.

• Establish automated testing and evaluation for accuracy, retrieval quality, hallucination, latency, reliability and cost.

• Implement appropriate authentication, access controls, audit trails, guardrails and data-security protections.

• Monitor and optimise model usage, response times, infrastructure costs and overall system performance.

• Maintain clean, tested and well-documented Python code.