Building an AI team: the skills behind the solution
When a business starts building an AI team, the hiring brief can quickly become: ‘We need an AI engineer.’
But what will that person actually build? And what needs to be in place for their work to succeed?
An AI feature in an existing product, an internal knowledge assistant and a bespoke machine learning model can call for different combinations of expertise. Defining the work first helps you understand which skills your team needs—and where your next hire can make the biggest difference.
Start with the outcome
Before choosing a job title, get clear on the problem you want to solve.
Are you helping employees find information faster? Automating a repetitive process? Adding a new capability to a customer-facing product?
Then consider what successful delivery involves. Building a prototype is one stage. Integrating it into existing systems, evaluating its performance and maintaining it once people depend on it adds further responsibilities.
A useful hiring brief should explain the intended outcome, the current environment and what the person will own.
Look beyond the AI layer
AI expertise is one part of the picture. Depending on the project, you may also need:
Data engineering: preparing reliable data and making it accessible to the systems that need it.
Software engineering: connecting the AI capability to applications and building a usable, dependable product.
Cloud and platform engineering: supporting deployment, monitoring, infrastructure and operating costs.
Security and governance input: defining appropriate access, data handling and oversight.
These responsibilities don’t necessarily require separate hires. Some may already sit within your team. The important step is identifying what is covered and where there are gaps.
That also helps keep expectations realistic. A role spanning AI development, data infrastructure, application engineering and ongoing operations needs a clearly defined scope and the right support.
Ask about delivery experience
When assessing candidates, explore how their experience relates to the work ahead.
What did they build? What were they personally responsible for? Did the solution reach production, and how was its quality assessed? What happened when it produced an unexpected result?
The answers can reveal how someone approaches practical trade-offs, collaborates with other specialists and takes responsibility for a working solution.
Match those questions to your project’s stage. An exploratory initiative may need someone comfortable testing ideas and managing uncertainty. An established product may place more emphasis on integration, reliability and ongoing improvement.
Build around your existing team
Your next hire should complement the capability you already have.
If you have strong software and platform engineers, specialist AI expertise may be the missing piece. If you have a promising prototype, the next priority may be the engineering support needed to make it dependable in everyday use.
Start with the outcome, map the responsibilities and identify the gaps. That creates a clearer brief—and a stronger foundation for hiring.
Planning your next AI hire? Get in touch with Nuage to talk through the skills your team needs.
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