If you have spent a few months looking at job openings in the tech industry, you have likely felt a strange sense of disconnect. On one hand, companies proclaim an urgent need to bring in Artificial Intelligence talent. On the other, the requirements they post seem straight out of a 2015 hiring manual.

There is an obvious short-circuit between how people are hired today and how software is built in the AI era. The root of the problem is not a lack of talent, but an outdated selection model.

1. The Title Bias: Profession vs. Skill

In the Spanish-speaking world, the term "Engineer" is usually strictly associated with a formal five-year university degree. In the Anglo-Saxon context, Engineer frequently describes the function or the operational skill (Prompt Engineer, Context Engineer, Systems Engineer).

Demanding a specific university degree in "Generative Artificial Intelligence Engineering" for applied roles is absurd: the discipline is just beginning to take shape in universities. Many of the people who today master model orchestration, context design, and the integration of AI-assisted architectures are self-taught. By filtering by "credentials", companies dismiss the practical talent that is already solving real problems.

2. The "N Years of Experience" Paradox

We keep seeing job postings demanding 5 or more years of experience in generative AI frameworks or specific languages.

The modern LLM ecosystem and post-ChatGPT assisted development has barely 3 or 4 years of real evolution. Measuring a developer's capability by the number of years they have been writing manual syntax is evaluating the tool of the past. In today's era, the key metric is not seniority in a language, but the ability to abstract, reason, and move fast to orchestrate tools.

3. Designed by the "Pre-AI" Era

Why are job descriptions so disconnected? Because, to a large extent, they are still written by IT leaders and HR departments trained in the traditional paradigm.

Software is conceived as an assembly line where each line of code is written character by character. They have not experienced the modern workflow, where the developer's role has migrated from writing code to specifying, evaluating, auditing, and integrating components generated by models.

4. The Obsolescence of Traditional Methodologies

For years, Agile and Scrum were the standard for structuring work. However, the bureaucracy of two-week Sprints, complex story point estimates, and static boards are becoming a bottleneck.

When building and validating a functional prototype takes a couple of hours instead of weeks, the development cycle changes completely. The speed of AI demands continuous workflows, dynamic prototyping, and immediate deployment. Adapting AI speed to Sprint bureaucracy is like putting bicycle brakes on a jet.

5. Less Résumé, More Live Execution

If inflated résumés and years of experience are no longer reliable metrics, how do you validate a candidate?

The answer is real-time execution-based evaluation. Instead of three rounds of theoretical interviews, companies should propose 2-hour practical tests.

In a couple of hours, a professional who masters today's AI-assisted ecosystem can:

Set up a database and authentication (e.g., Supabase).

Integrate a payment flow or email sending.

Deploy the architecture to the cloud (e.g., Vercel) and deliver a functional MVP or PWA.

That ability for rapid integration, problem-solving, and real-time deployment is the true acid test.

Conclusion

The market does not need more bureaucratic filters; it needs to adapt its processes to the speed of modern development. The companies that understand that "AI Engineering" is an execution skill and not an accumulated title will be the ones that truly manage to attract the talent leading this transformation.