Building your AI Engineering Team? Let's discuss AI Mindset, Culture, and Responsibility: A Guide for CTOs and Engineering Managers

Across Australia, AI has moved from experimentation to practical application. Organisations are embedding AI into products, services, and operations, and at the centre of this shift is the AI Engineer. These professionals operate at the intersection of software engineering, data, and machine learning, designing systems that adapt, learn, and deliver tangible business value.

CTOs and Engineering Managers building their first AI team face a dual challenge: identifying candidates with the right technical skills and ensuring alignment with the mindset and culture of the organisation. Early hires define the team’s capabilities, set standards, and shape the way your AI practice collaborates, innovates, and delivers value.

What Makes a Great AI Engineer

AI Engineers combine coding expertise, machine learning knowledge, and deployment experience. They understand how to take a model from concept to production while designing systems that scale and perform reliably.

But the strongest AI Engineers do more than code. They think in terms of solutions architecture, balancing immediate project needs with future technology changes. They ask questions like: How will this integrate with existing data pipelines? How can inference be scaled cost-effectively? What emerging tools or models could improve the solution in the future?

For first-time AI team leaders, this architectural thinking is critical. Early hires not only execute projects but also establish frameworks, standards, and practices the team will follow for years. Selecting engineers whose mindset aligns with your organisation’s culture ensures that technical expertise is paired with collaboration, curiosity, and strong communication across diverse teams.

Skills, Mindset, and Responsible AI

When assessing candidates, look for end-to-end problem-solving: building data pipelines, integrating APIs, deploying models, and implementing Responsible AI practices such as bias monitoring, fairness, explainability, and ethical decision-making.

Project-based discussions often reveal more than traditional interviews. Ask candidates how they approach deployment, manage trade-offs between speed, accuracy, and fairness, and future-proof solutions. Cultural alignment is equally important. AI Engineers work across product, data, and executive teams. The most successful hires are collaborative, adaptive, proactive, and able to translate complex technical concepts, including Responsible AI considerations, into actionable business insights while embedding a responsible AI mindset within the team.

Australian AI Skills landscape

The AI talent pool is growing but still limited, making careful recruitment critical. Strong AI Engineers often come from adjacent fields: software engineers upskilling in ML, data scientists gaining deployment experience, and ML engineers productionising models, including GenAI and large language models. Emerging roles like AI Product Engineers, LLM Engineers, and MLOps Specialists are bridging gaps between data, design, delivery, and Responsible AI practices.

For leaders building a first AI team, hybrid profiles that combine technical depth, architectural thinking, cultural alignment, and a Responsible AI mindset are invaluable. These hires set standards, mentor future engineers, and embed practices that support long-term scalability, ethical innovation, and collaboration.

Final Thoughts

Recruiting AI Engineers isn’t just about technical skills. It’s about hiring people who can deliver today, scale for tomorrow, and thrive within your team and organisation’s culture while embedding Responsible AI practices from the outset. Early hires define both the capability and character of your AI practice, influencing how solutions are designed, deployed, and maintained ethically.

If you’re looking to establish or expand AI capability in your business, now is the time to connect. I can provide insights on the Australian AI candidate landscape, discuss your requirements, and help you identify talent that has the skills, mindset, cultural fit, and Responsible AI awareness to succeed in your organisation.

I appreciate your time. Any questions, please reach out

Gino Lancaster Chief AI Talent Officer, AI Talent Australia