In 2024 everyone wanted a chatbot. In 2025 everyone wanted a “copilot”. In 2026 the conversation is about AI agents: software that does not just answer questions, but takes actions, calls systems and completes tasks. 🤖
That shift changes who you need to hire. A clever prompt in a demo is easy. An agent that can safely update a customer record in your CRM at 2am, with an audit trail, is a very different job.
The roles we are seeing most demand for
AI Engineer (applied)
Builds production AI features using large language models, retrieval, tools and APIs. Strong Python, cloud and software engineering habits. Think “engineer who uses AI”, not “researcher”.
AI Platform or MLOps Engineer
Owns the plumbing: model hosting, evaluation pipelines, monitoring, cost control and security on AWS, Azure or Google Cloud. The person who stops your AI bill from looking like a phone number.
Data Engineer
Still the unsung hero. Agents are only as good as the data they can reach. Databricks, Snowflake, Fabric, dbt and solid data modelling remain in demand.
AI Product Owner
Decides which processes are worth automating, defines success and keeps humans in the loop where they need to be.
AI Governance and Risk Specialist
Maps AI use to privacy, security and emerging Australian guidance on safe and responsible AI. Increasingly common in banking, insurance, health and government.
Platform specialists with AI skills
Salesforce Agentforce, ServiceNow AI agents, Microsoft Copilot Studio and SAP Joule all need people who know the platform and the AI layer. These hybrid profiles are rare and move quickly.
How to tell real AI skills from buzzwords
- Ask about evaluation. “How did you measure whether it worked?” separates builders from tourists.
- Ask about failure. Real practitioners have stories about hallucinations, cost blowouts and permission mistakes.
- Ask about data access. Who can the agent act as, and what can it touch?
- Look for engineering basics. Version control, testing and monitoring matter more than a fancy certificate.
Build, buy or borrow?
Most Australian organisations are not hiring 20-person AI teams. A practical pattern we see is a small permanent core (product owner, lead engineer, data engineer) supported by specialist contractors for build sprints. It keeps knowledge in-house without overcommitting before the use cases prove themselves.
👉 Planning your first agent into production? Share your AI requirement and we will help you shape the team. Want the wider context? Read our article on how AI is transforming hiring.
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