As the data and AI revolution accelerates, the demand for specialized talent has reached unprecedented levels. For many companies, finding and hiring the right engineers is a significant bottleneck to expansion. It's particularly challenging for non-technical hiring managers who need to evaluate a candidate’s expertise in complex and rapidly evolving fields like generative AI and agentic AI.
If you're trying to hire an LLM engineer or other data and AI professionals, and struggling to find the right candidates, you're not alone. Currently, AI is ranked as one of the most difficult skill sets to find in qualified professionals. The competition for talent is fierce; everyone from Fortune 500 companies to startups is vying for the same individuals with this highly specialized knowledge base.
To help you navigate this competitive landscape, we have curated a list of ten essential interview questions for hiring LLM engineers and other AI experts. These prompts are designed to screen for deep technical understanding, practical experience, problem-solving ability, and cultural fit for a high-performing data team.
Essential Screening Questions for Technical Acumen
While you don't need to be an expert in machine learning, you do need to understand how to probe a candidate’s practical knowledge. The goal of these questions is to move beyond buzzwords and assess the depth of their hands-on experience and their ability to apply theory to real-world problems.
Ask your LLM engineer candidates:
1. Can you explain the difference between Generative AI and Agentic AI?
Generative AI creates new content like text or images in response to user prompts, acting in a reactive manner. In contrast, agentic AI operates autonomously to achieve complex, long-term goals, making independent strategic decisions.
A strong candidate will clearly articulate this distinction, showing they understand which model is best suited for different business challenges.
2. Describe a time you had to fine-tune a large language model.
This question is highly specific and is excellent for screening a potential LLM engineer. It moves beyond generic claims of "working with AI" and gets to practical experience.
Make sure candidates can answer details on the process, such as what data was used, what was the specific business goal, and what frameworks were involved.
3. How would you handle a production model that is consistently giving poor predictions?
This probes the candidate's real-world skills in MLOps and debugging. An ideal response outlines a systematic approach, such as checking the data pipeline or monitoring the model's inputs. It shows they understand the life cycle of an AI model beyond its initial creation.
Questions to Assess Problem-Solving and Cultural Fit
While technical skills are certainly vital, they aren't the only factors to assess when hiring data and AI talent. Engineers should also possess "soft skills," like clear communication, as they will need to share ideas and reports with non-technical stakeholders.
To get a sense of how a potential hire will mesh with your team, include these interview questions:
1. What is a complex data project you led that focused on collaboration between data engineers and data scientists?
This question assesses the candidate's experience in a cross-functional team. Often, a successful AI project requires seamless collaboration between data engineers and data scientists. LLM engineer candidates should be able to clearly define these different roles and explain how they support one another.
2. Describe a time an AI project failed. What did you learn?
This question assesses humility, resilience, and a learning mindset. After all, the AI landscape is full of experimentation and uncertainty, and failure is a part of many business iterations. A strong candidate should be able to admit their failures, while discussing the lessons they learned from their experience and how it helped them grow.
3. How do you stay current with the rapidly evolving AI landscape?
The field of AI changes almost daily, with new models and frameworks emerging constantly. A top-tier LLM engineer should be able to cite specific resources, research papers, or communities they follow.
This answer will demonstrate their dedication to continuous learning and upskilling, which is a must-have trait.
Strategic and Value-Driven Questions
The final set of questions screens candidates for business acumen and the ability to link technical work directly to commercial value. Ask your LLM engineer or other data and AI candidates the following:
1. What are the ethical considerations you take into account when deploying a new AI model?
This question assesses responsibility and foresight. A sophisticated AI engineer understands issues like bias in training data, data privacy, and model fairness. Their response should show they view AI as a powerful tool with significant real-world consequences and that they build with safety and alignment in mind.
2. How do you measure the ROI of your AI projects?
Finally, a strong LLM engineer understands that their work must ultimately drive business value. Their answer should focus on measurable outcomes like cost savings, revenue generation, or efficiency gains. This demonstrates that they think like a business partner, not just a programmer, and they will likely make an excellent addition to your team.
Accelerate Your Search for World-Class AI Talent
Successfully hiring AI experts like LLM engineers is incredibly difficult due to the severe skills shortage and the immense competition for talent. The total first-year cost to hire full-time talent can easily run between $150,000 and $200,000. Plus, the hiring process can take months.
That's where The JADA Squad comes in. We provide world-class talent in data and AI, operating on a subscription-based model that reduces overall costs and ensures you get the expertise your team needs, right away. If the lengthy timeline and high cost of traditional recruiting present a barrier to your company's growth, contact JADA today.
Frequently Asked Questions
What does LLM stand for?
LLM stands for "large language model," a type of artificial intelligence that processes and generates natural human-like text after being trained on precise linguistic datasets. LLMs are used for content creation, chatbots, translation, analysis, and more.
What is an LLM engineer?
LLM engineers are a type of software engineer specialized in building systems using Large Language Models and other generative AI systems. They focus on applications generating new content, such as text, code, or images that are created by taking user prompts as input. Unlike engineers who focus on analysis, they work with models like GPTs to ensure their generative capabilities actually work with real-world applications.
What is the salary of an LLM engineer?
Generally, the hiring of a full-time generative AI engineer, including an LLM engineer, costs between $114,000 to $158,000 in base salary. However, this range will vary greatly depending on their experience, location, and the company's size.
What is the difference between an LLM engineer and an AI engineer?
AI engineer is a broader category that includes any engineering professional working with artificial intelligence systems, including LLM engineers. LLM engineers specifically work with Large Language Model systems that generate language-based content.
