Senior AI Engineer

About the Role

We're looking for one thing: someone who builds. You use AI as a force multiplier — Claude or Copilot open on the second screen, prompting before Googling — and you ship real systems into production, not decks and prototypes.

Most AI consulting stalls at the POC. We're hiring for the opposite problem: people who can take an idea from a client conversation to something running in an enterprise environment, with the governance, security and reusability that survives handover.

You'll be the one making it. But you'll also be in the room when the problem is still fuzzy, so you need enough consulting instinct to run a discovery session, and enough architecture judgement to build something we don't have to rebuild.

What You'll Do

• Build and productionise AI-enabled solutions — LLM apps, RAG, agents, automation — in real client environments

• Shape the problem — sit with clients early and translate technical options into business terms

• Make architecture calls that hold up — secure, explainable, supportable, reusable

• Drive efficiency — use AI tooling to compress delivery timelines, and know when not to use it

• Lift the team — raise AI fluency in the people around you

What We're Looking For

Essential:

• 3-5+ years building software or data systems, with recent hands-on delivery

• Demonstrable AI tool fluency in your current role — you can talk specifics about what you've built and what you've abandoned

• Something you've taken to production, not just to demo

• Comfortable client-facing: you can run a workshop, not just attend one

• Experience in Agile delivery and comfort with rapid iteration

AI-Specific Competencies (at least 3 of the following):

• LLM-based applications in production (RAG, prompt engineering, evals, fine-tuning)

• Hands-on with AI coding assistants and AI-driven DevOps pipelines at team scale

• Familiarity with AI/ML platforms (Azure OpenAI, AWS Bedrock, Vertex AI, or equivalent)

• Agentic AI frameworks (LangChain, AutoGen, CrewAI, or your own)

• Responsible AI: risk frameworks, bias mitigation, explainability, and the emerging Australian regulatory context

• Track record of measuring AI ROI and operationalising AI at enterprise scale

Desirable

• Certifications in cloud AI platforms (Azure AI Engineer, AWS Machine Learning, Google Cloud AI)

• Background in data science, MLOps, or AI product management

• Published thought leadership or speaking experience on practical AI adoption