Navigating Tech Recruitment: Strategies for Identifying Developers with Forward-Thinking Approach
AI's impact on software development has shifted the spotlight to a pressing question in the tech industry: how can you tell the difference between developers who use AI effectively and those who rely on it excessively?
Let's dive into this issue, building upon our previous articles, If AI Can Write Code, What's Left for Developers? and AI Is Changing How We Code.
In today's world, the line between someone who "looks like a dev" and someone who truly is one has become blurry. To an untrained eye, a junior dev with a code assistant might seem just as effective as an experienced developer. But the difference is huge, especially when it comes to an AI-Augmented Developer who truly understands how systems behave, writes code that scales, supports businesses, and can prevent future problems.
These professionals are certified, experienced, and grounded in computer science fundamentals. They understand business logic, software architecture, and can balance agility with long-term maintainability. They're pragmatic, but principled, shipping quickly while leaving organized documentation, focusing on what needs to be fast and what needs to be future-proof.
This guide, created in collaboration with Pablo Bucci, Recruiting Lead at Inclusion Cloud, explains how to identify and hire top-tier AI-augmented talent.
Trying to deny that people use AI at work is a battle already lost. AI has become a foundational force in businesses, transforming how they build, scale, and deliver digital products. At Inclusion Cloud, we've adapted our recruiting engine to this reality. We integrate AI into the process, but always with an ethical, responsible perspective, ensuring we create real value for our clients.
What's Changing in Tech Hiring Right Now?
Remote processes are being reexamined, especially as it becomes harder to validate who's really doing the work. The rise of AI has introduced a new challenge: distinguishing between people who work with AI and those who let AI do the work.
In interviews, some patterns are hard to ignore, such as long pauses, rehearsed answers, or struggling during real-world situations or cross-functional dynamics. These moments often indicate that the candidate is relying on an AI assistant in the background, rather than drawing on their own experience.
This issue becomes even more obvious with take-home coding challenges. "Too often, we get generic answers that lack context, documentation, or any real architectural reasoning," says Pablo. These are signs that the candidate may be relying on AI without truly understanding the structure behind what they're building.
He adds, "We then cross-check that information with their resume, LinkedIn profile, and other public data. We look at what projects they've actually worked on, what their role was, what technologies they used, and what goals they helped achieve."
And that's where many teams run into real problems-hiring someone who performs well on a test, only to realize days or weeks later that they can't work in the complexity of a real-world work environment.
At Inclusion Cloud, we believe this shift is just getting started, and AI skills will become a standard part of the hiring process. Companies will not only assess technical and soft skills but also evaluate how effectively candidates collaborate with AI tools.
This means looking at prompt engineering, tool fluency, and the ability to use AI to accelerate delivery while still producing scalable, testable, and well-structured code. Knowing how to ask the right questions, use context, and guide an assistant is already becoming a key differentiator in identifying top-tier developers.
AI is here to stay-and that's a good thing, as long as it's used right. We don't believe AI should be restricted or banned. When used with purpose and expertise, it can be one of the most powerful accelerators of productivity we've seen in decades.
Two Types of AI Usage in Development
Developers tend to fall into two main categories when it comes to AI use, and each one requires a different approach during the hiring process:
1. Low-code platforms with embedded AI
These are enterprise systems like ServiceNow, Oracle, SAP, or Salesforce where much of the work is performed through configuration, drag-and-drop logic, and predefined components. AI is often built into the platform to assist with automation and speed up delivery.
"In these environments," Pablo explains, "most of the heavy lifting is done by the platform itself. What matters is asking what automations the candidate built and how. But it's just as important to know if they understand the business and workflows, because that's how you know if the automation made sense."
Identifying top talent in these environments is more straightforward because there are clearer benchmarks. "If they're certified and have experience working on real projects, you can dig deeper: what did they build, what problems did they solve, and how would they approach similar situations today?"
These conversations allow recruiters to go beyond buzzwords and understand the candidate's actual involvement. By presenting hypothetical scenarios or discussing platform-specific edge cases, it becomes clear whether the developer understands the structure-or was simply following pre-set steps.
2. Generative AI and custom code
The second category is more complex and involves developers using AI tools like ChatGPT or GitHub Copilot to generate custom code-whether backend logic, frontend components, or full-stack services.
"This is where things get trickier," Pablo notes. "We've had interviews where candidates are clearly reading AI-generated responses or taking long pauses because they're prompting something in another tab."
That's when his team changes approach. "In those cases, we ask real-world questions where they need to apply best practices or explain how they solved a specific problem in a project. Then we cross-check that information with their resume and LinkedIn. That's where you really see if they were hands-on or not."
Validating these candidates requires deeper technical assessment. There's no certification for good architecture or clean abstractions. "Too often," Pablo adds, "we get generic answers that lack context, documentation, or any real architectural reasoning."
To properly assess candidates in this category, the team relies on live coding challenges and peer-level technical interviews. "We observe how they solve problems, how they explain their thinking, and whether the solution makes sense. That tells us a lot more than just seeing lines of code."
How We Built a Recruiting Engine for the AI Era
We didn't wait for the wave to crash. We made an early decision to adapt our recruiting engine to meet the demands of this new era of software development.
In the first stage, we developed our AI-powered recruiting engine to identify top talent across the pipeline. It uses advanced AI tools to match job requirements with more than one million candidates in our database, analyzing everything from resumes to public data sources like LinkedIn and GitHub, aiming to find the best fit based on data and algorithmic predictions of future performance.
In the second stage, we expanded our candidate profiling to include how each professional performs using AI. Beyond the traditional technical and soft skills, we now evaluate AI usage as a key indicator of adaptability and long-term potential. It's become essential for identifying developers who can truly thrive in this new landscape of software development.
Once we've identified a potential match, the human side of the process begins. Our HR team leads interviews, focusing on how a candidate communicates, how they explain their decision-making, and how they've collaborated with teams in the past. Cultural fit and adaptability often tell us more about long-term success than technical skills alone.
Then comes the technical deep dive. Every candidate is evaluated by our senior engineers through conversations grounded in real-world challenges. We ask how they made architectural decisions when scaling a payment system under high traffic, or how they handled version conflicts during a system migration. These discussions reveal how candidates work under pressure, how they evaluate trade-offs, and whether they can tie technical decisions back to business value.
We also take a close look at how they incorporate AI into their workflow. Do they understand prompt engineering as more than trial and error? Can they refine outputs to meet architectural and security standards? We ask which models they typically work with and why. This helps us assess how well their tools and approach align with a company's existing stack and governance model.
Sometimes, we go even deeper. How would they prompt a language model to build a data transformation pipeline? Would they trust it to write production-ready Terraform scripts? If not, what steps would they take to review, test, and refactor? The answers reveal not just their level of AI proficiency, but also how they think-how they evaluate risks, apply standards, and determine what still requires human oversight.
What we've learned is this: good developers can generate code. Great developers design systems.
What to Look For: Spotting AI-Augmented Developers
So, how can you tell the difference between someone who uses AI effectively and someone who simply leans on it excessively? According to Pablo Bucci, the key is digging beyond resumes and surface-level answers.
- Ask for specific examples: We avoid vague or theoretical questions. We ask about real scenarios: What was the business problem? What technical decisions did they have to make? What would they do differently now? This helps reveal whether the person actually contributed to the solution, or if they're just repeating/memorizing what they've seen elsewhere.
- Check for technical consistency: We always cross-check their answers with their public profiles. Resume, GitHub, LinkedIn. If they describe backend decisions but everything on their resume is front-end work, that's a red flag. The story needs to match the skills.
- Use live code challenges: This is where real differences start to show. By observing in real-time how candidates perform, we can evaluate their problem-solving skills, reactions, and solutions. We understand that live challenges can make people nervous, but they help shift the focus away from what might be happening in another tab and toward how the candidate actually thinks, reacts, and solves problems.
- Review the technical depth of their answers: Not all working code is good code. That's why we look closely at the reasoning behind a candidate's decisions-not just the outcome. Generic or vague solutions are often a red flag that an AI tool was used without proper validation.
- Pay attention to non-verbal details: In remote interviews, it's essential that the camera must be on. You can tell when someone is reading, making strange silences, or oddly paced answers. These small cues often reveal whether someone isn't fully present or engaged.
- Test communication and ownership: Technical skills are just part of the picture. We use situational questions that reflect real-world collaboration to evaluate a candidate's communication abilities and ownership of their work.
- Always include a technical evaluation by senior engineers: Every candidate at Inclusion Cloud goes through a rigorous technical evaluation led by our senior engineers. This step is what truly sets our process apart. It simulates the kind of challenges and dynamics that developers face in clients' projects, focusing on their problem-solving skills, real-world experience, and technical depth.
- In the hiring process, companies will assess not only technical and soft skills but also evaluate how effectively candidates collaborate with AI tools, as this will become a standard part of the process with AI skills being crucial.
- To identify top-tier AI-augmented talent, it is essential to dig beyond resumes and surface-level answers. This can be done by asking for specific examples, checking for technical consistency, using live code challenges, reviewing the technical depth of answers, paying attention to non-verbal details, testing communication and ownership, and always including a technical evaluation by senior engineers.
