On 30 September, Barcelona Code School Product Manager Alena Dzinman hosted a live Q&A with school founder George Kovalev and Career Advisor Diane Serra. Together, they discussed what AI is changing in hiring, why emerging AI roles can lower the barrier to entering tech, and how experienced professionals can remain valuable as tools and roles evolve.

AI is changing work, not removing the need for judgement

Diane described a first wave of urgency: leaders wanted visible AI adoption, and some companies assumed that using AI meant they could remove design or other specialist roles. Then the limitations became clearer. Teams still needed people who could judge quality, understand context and take responsibility for the result.

She also sees prospective clients asking agencies for “AI” without being able to define the problem they want to solve. That gap creates an opening for professionals who can move the conversation from a fashionable tool to a useful outcome.

“You still need human discernment.”

- Diane Serra

The opportunity, then, is not simply to know how to open an AI tool. It is to help a company decide where AI is appropriate, what the solution should cost, which risks need controls and how success will be measured.

New AI roles create an opening for beginners

AI-related titles remain inconsistent. Similar work may appear under AI Automation Specialist, AI Agent Developer, AI Adoption Manager, AI Transformation Manager, AI Automation Engineer or AI Implementation Specialist. Diane's advice was to watch the language companies use and keep your CV and LinkedIn positioning current.

“The one I'm seeing most is the AI Automation Engineer.”

- Diane Serra

George explained why this changing market can favour a newcomer. Companies are trying to become AI-first or AI-native and automate work across departments, but the professional field is too new to have a large pool of people with long, directly relevant careers. There is competition, including from candidates around the world, but it is less entrenched than in established technology roles with rigid experience ladders.

“And here's the interesting part: in AI agents and automation, almost nobody has years of experience, because the field itself is new.”

- George Kovalev

That does not make the work automatic or guarantee a job. It removes one of the usual barriers: beginners are not always competing against professionals with ten years in the exact same role. Employers need proof that you can do the work, and a portfolio of functioning AI agents and automations can provide it.

“It's almost like the word "junior" barely applies here.”

- Diane Serra

Can junior developers and designers still get hired?

Yes, but the route may be more strategic than sending the same application to every famous technology company. Diane pointed to midsize companies, startups, small businesses and local government as places where entry-level talent can solve real problems. Not every organisation needs or can afford an all-senior team.

Freelancing can be another route. A new developer or designer can begin with small-business clients, build experience and a portfolio, and grow beyond the junior label through delivered work. Company size also changes what “junior” means: in a small team, responsibility can expand quickly; in a large organisation, progression may involve many formal levels.

“So don't get discouraged if you're just starting out.”

- Diane Serra

What employers want now

For an early-career candidate, employers need proof of practical ability and enough clarity to see where that person fits. For an experienced engineer, Diane looks for something more: the ability to understand what the business needs, propose a solution and continue improving it.

This is where technical execution meets initiative. A strong candidate does not merely say, “I know AI.” They can say, “Here is the process I studied, here is the problem I found, here is the system I built, and here is how I know it helped.”

“From my day-to-day experience, the best developers I have right now are the ones who know AI - meaning they can tell me what the business needs and show me what that looks like, then keep building on the solution they proposed.”

- Diane Serra

Your CV, LinkedIn and portfolio should tell one story

A portfolio cannot do all the work if the rest of your professional story points somewhere else. Diane recommended treating your CV, LinkedIn profile and portfolio as three parts of the same narrative: what you know, what you have done and what you want to do next.

That story must also feel true to you. If you can explain it clearly in an interview, a hiring manager can understand how you might contribute to a team. If AI is genuinely part of your skill set, include it in your positioning, but support the label with projects and specific examples.

“The clearer and more coherent that story is across all three, the more prepared you'll be walking into an interview, because you'll be able to speak to it confidently.”

- Diane Serra

Your previous career can be your advantage

Diane entered tech from design, teaching and commercial arts. Her experience since completing a bootcamp has shown her that the industry has room for many backgrounds. Knowledge from healthcare, finance, education, logistics, hospitality, design or another field can help you recognise problems that a purely technical candidate might miss.

The useful combination is domain knowledge plus the ability to build. Take what you already understand about people, processes or an industry; add technical skill; then use both to create better solutions. A career change does not have to erase what came before it.

“Take what you already know, add what you learn in a bootcamp, and bring both to your career.”

- Diane Serra

In-house role, consulting, or both?

George described two valid paths. A larger company may hire someone permanently to build and improve automations, while another business may bring in a consultant for a defined implementation. Some professionals may combine an in-house role with carefully scoped consulting work.

The first step is the same in each case: understand the company's processes, needs, bottlenecks and constraints before choosing a tool or proposing an implementation. Small, recurring tasks may benefit from in-house ownership; a complex one-off project may justify external expertise.

“Yes - the first step is always understanding their processes, their needs, their bottlenecks, and the problems they're facing.”

- George Kovalev

What should you do next?

1. Choose a direction. Decide whether you want to enter development, design, AI automation, product or another part of tech. You can refine it later, but you need a starting point.
2. Map your transferable experience. List the industries, users, processes and problems you already understand.
3. Study real vacancies. Search several related titles and compare the responsibilities and evidence employers request.
4. Build one complete project. Solve a real, bounded problem and document the decisions, tools, tests and result.
5. Align your public story. Make your CV, LinkedIn and portfolio support the same target role without pretending to have experience you do not have.
6. Apply beyond the obvious companies. Include smaller businesses, startups, public organisations, agencies and freelance clients where your combined skills may be especially useful.

The field is open, but proof matters

The panel did not promise an effortless route or a guaranteed job. Competition, persistence and the quality of your work still matter. The opportunity is that AI automation has a lower entry barrier than many established technology tracks: the roles are new, demand is spreading across industries, and no-code tools let beginners build substantial systems without first spending years learning software development.

You do not need to predict the final job title. You need to understand a business problem, build something useful and explain the value clearly. The AI Agent & Automation Bootcamp is designed to help a beginner produce exactly that kind of evidence, while experienced professionals can use the same skills to stay valuable as the tools change.