Why AI hiring is harder than it looks
The problem with hiring AI engineers in 2026 is that the titles are not standardized. An AI engineer at one company might be a research scientist building models from scratch. At another company, the same title means someone who writes prompts and connects APIs. Both are legitimate roles. But they are completely different skills.
If you hire without being precise about which skills you actually need, you will pay a senior rate for someone who cannot do the work your project requires.
Define the role before you post it
Start by answering these questions precisely. Do you need someone to build and train ML models from scratch, or to deploy and integrate pre-trained LLMs? Do you need a researcher or an engineer? Do you need someone who understands data infrastructure, or just model development?
The answers determine whether you are hiring an ML engineer, an AI solutions engineer, a data scientist, a data engineer, or an LLM integration specialist. These are four different roles with different skill sets and different market rates.
The evaluation process that actually works
Give a take-home technical problem that mirrors your actual project. Not a LeetCode exercise.
Ask them to explain their solution as if to a non-technical stakeholder. Communication is as important as technical skill.
Ask specifically about their experience with production deployment, not just prototyping.
Ask what went wrong on a past project and how they handled it.
Request examples of systems they built that are still running in production.
"Anyone can demo a working prototype. The differentiator is whether they have deployed something that has been running for six months without breaking."
When hiring does not make sense
Full-time AI engineering hires are expensive, take three to six months to recruit, and carry risk if the project scope is not yet clear. For specific, time-bounded projects, working with an on-demand specialist through a partner like Agintex is often faster, lower risk, and more cost-effective than a permanent hire.
Our Experts on Demand service places vetted AI engineers, ML specialists, and data engineers directly into your team within 48 to 72 hours of a brief. No recruitment overhead, no long-term commitment until you are ready.
About author
Tobias oversees software, product engineering, and connected systems at Agintex. He writes about technical architecture, IoT integration, UI/UX engineering, and what it actually takes to ship a product that works at scale.
Tobias Lane
Head of Engineering
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