Hiring AI Engineers: What Talent Acquisition Leaders Need to Know

So, you want to hire an AI engineer. Easy, right? Just find someone who can whisper sweet nothings to ChatGPT and call it a day? Not quite. Hiring for AI feature development is more than just finding someone who knows how to craft a clever prompt. You need engineers who can integrate AI into real-world products. 

In other words, you’re looking for a builder, not just a coder. Someone who can take the ever-evolving landscape of AI models and make them work for your product in a structured, scalable, and cost-efficient way.

This guide will walk you through what an AI engineer actually does, what skills they must possess for on the job success, and most importantly, how to interview them effectively to find the best fit for your growing team.

What Does an AI Engineer Do?

AI engineers don’t just tinker with AI models in a dark room, conjuring magic out of code. Their day-to-day work involves real-world problem-solving—designing AI-powered features that real people actually use in their day to day. Their tasks often include:

• Creating useful prompts: This is a skill. It requires proper context and design. A good prompt is structured and scalable. Inputs must be standardized.
• Evaluating the outcomes of these prompts: Ensuring that what the prompt generates is applicable and accurate. Outputs must also be standardized and predictable before they are put into the product.
• Optimizing performance: Tweaking prompts to not cost a fortune in API calls.
• Collaborating with product teams: AI doesn’t live in a vacuum—it has to fit into a broader product and user experience. 
• Keeping up with AI advancements: This field moves fast. Engineers need to stay on top of new models and best practices.

Who Hires AI Engineers?

Everyone and their grandmother wants AI now. But seriously, AI engineers are in demand across many industries:

• Tech Companies: Chatbots, recommendation systems, search engines, you name it.
• Finance: Fraud detection, algorithmic trading, risk assessment.
• Healthcare: Medical imaging analysis, predictive diagnostics, personalized treatment plans.
• Retail & E-commerce: Personalized shopping, inventory forecasting, AI-powered customer service.
• Media & Content: AI-generated transcriptions, recommendations, and translations.

The demand for AI engineers is skyrocketing right now and we don’t expect it to slow down any time soon. 

Key Skills to Assess in an AI Engineer

Hiring for an AI engineer has many similarities to hiring for a traditional backend engineer, but there are some notable differences you must test for. You’re looking for someone who has deep expertise with AI models and knows how to harness their power to make products better. An AI engineer should understand:

✅ Knowing where AI actually makes sense

Can they distinguish between a genuine AI use case and a forced implementation? An experienced engineer won’t simply use large language modeIs (LLMs) for every use case. They will know when to use LLMs versus machine learning versus simple rules. They will have a deep understanding of which technologies fit which use cases to provide the best product and user experience. 

✅ Data Pipeline & Storage Management

AI models are only as good as the data they’re fed. A strong candidate understands how to clean, preprocess, and structure data to prevent garbage in, garbage out scenarios. They also know how to store and retrieve data efficiently, ensuring scalable and cost-effective solutions. A comprehensive understanding of pipelines, data management, error handling, and working with an external API make this skill critical to building a successful product. 

✅ Prompt Engineering & Output Structuring

Crafting the right prompts isn’t just about writing a question. It’s about understanding how AI models interpret language and context to ensure consistent, structured, and usable outputs. Candidates should demonstrate how they refine and chain prompts to create workflows that yield reliable and predictable results.

✅ Cost & Performance Optimization

AI models can be resource hogs. A skilled AI engineer doesn’t just build something that works—they optimize for performance and cost-efficiency. This includes minimizing API calls, reducing redundant computations, and balancing accuracy with latency to ensure AI implementations are both effective and scalable.

✅ Backend Engineering Basics

AI isn’t magic; it still runs on software fundamentals. A great AI engineer should have strong backend skills, including API integrations, error handling, testing, and debugging. They should be able to deploy AI-powered features in production environments without causing system failures or costly downtimes.

A good AI engineer is 80% backend/data engineer and  20% AI specialist—because at the end of the day, AI is just another tool in the software stack.

How to Structure the AI Engineer Interview

Spend the right amount of time testing the right skills

• An AI engineer interview process should match the skills and knowledge that will be expected on the job. Structure the interview process to mimic the balance of skills. 
• For example, if the candidate will be expected to spend 80% of their time in the role utilizing backend data skills and 20% leveraging AI expertise then the time spent interviewing should correspond accordingly. 

Dive into discussion about past experiences

• Ask about previous AI projects and dig into their specific role.
• Gauge their ability to translate AI’s abilities into real business impact.

Make interviews realistic and relevant

• Give candidates a business problem they would actually face on the job.
• Test how they reapply their knowledge and skills to your specific business context.
• Present edge cases and challenges to see how they handle unexpected AI behaviors.

Use AI tools in the coding interviews

• Incorporate the usage of real AI tools into the interview process.
• Ask candidates to build, fine-tune, or evaluate an AI feature using commonly available APIs.
• Assess their workflow efficiency—how do they interact with AI tools to iterate and improve results?

Ensure interviews are project-based

• Request the candidate to build a small AI feature that aligns with your company’s needs.
• Structure the interviews so that each technical discussion builds upon the last, allowing for continuity and deeper analysis.
• Collaboratively code to work together in real time and understand the candidate’s thought processes and working style.  

How CoderPad Helps You Hire AI Engineers

Finding and assessing AI engineers may seem like a daunting and complex task, but CoderPad simplifies the process for talent acquisition teams. With CoderPad’s platform, hiring managers can quickly evaluate candidates on real-world AI engineering challenges in a highly realistic paired programming environment. The interviewer can test all of the key skills (and many more!) in a structured, objective way, including how the candidate leverages AI tools with the built in ChatGPT integration. 

From testing data pipeline skills to assessing prompt engineering expertise, CoderPad is here to ensure that your hiring team can make confident, data-driven decisions when selecting who to bring on the team.

Final Takeaways

• AI engineers require deep knowledge of both backend engineering and AI models. 
• AI hiring will only grow—think back to mobile engineering hiring. It started in the 2010s and is still booming today. 
• Interviewing should mimic real work—use hands-on, practical challenges that leverage AI and business context.  
• CoderPad’s products are designed to help you assess AI engineering talent with ease and confidence. 

Get a demo of how you can test for real-world AI skills in a realistic interviewing environment. With CoderPad, you’ll have the confidence that you’re hiring engineers who can build scalable, cost-effective, and actually useful AI-powered features—not just those who can write a clever prompt.