After nearly eight years at the same company, I am now looking for my next professional opportunity.
That would already be a significant change on its own. But there is one small detail: I am returning to the job market at a time when Artificial Intelligence can write code, create tests, explain systems, find bugs and, occasionally, invent a library that has never existed with complete confidence.
As a Senior Full-Stack Developer, I cannot look at this moment with fear or excitement alone.
I need to look at it through the lens of experience.
And that is where an idea I have been thinking about comes in: vibe architecture.
First came vibe coding
Vibe coding popularised a different way of building software.
You describe what you want, have a conversation with an AI, accept some suggestions, reject others and, after a series of prompts, something appears on the screen.
Sometimes it is exactly what you asked for.
Sometimes it builds an entire authentication platform when all you wanted was to change the colour of a button.
It is an incredibly powerful approach for prototyping, experimentation, automation and quickly validating ideas. The distance between imagining a feature and seeing it work has become much shorter.
However, there is an important difference between:
“It works on my machine.”
and:
“We can put it into production, process payments and support thousands of users.”
That difference is usually called software engineering.
So, what is vibe architecture?
To me, vibe architecture means using the speed of AI without abandoning technical responsibility.
It means allowing AI to help with implementation, investigation, documentation and testing, while the engineer remains responsible for questions such as:
- Where should each responsibility live?
- How should components communicate?
- What happens when a dependency fails?
- How do we protect sensitive data?
- How do we observe the system in production?
- How will the application be maintained two years from now?
- Why has the AI created six abstractions for a twelve-line function?
Vibe architecture does not mean drawing a few boxes, adding some arrows and calling everything a microservice.
It means turning intention into structure.
AI can generate an API very quickly. But someone still needs to decide whether that API should exist, what boundaries it should respect, how it should be versioned and what happens when it receives fifty thousand requests per minute.
AI can write code.
Architecture is deciding which code should be written.
Experience has not lost its value
There is a recurring narrative that AI will make experienced developers less necessary.
I see it differently.
The more code we can generate, the more important it becomes to distinguish useful code from code that is merely convincing.
Someone without much experience may ask:
“Build me a scalable platform for millions of users.”
An experienced engineer is more likely to ask:
“How many users do we have today?”
That second question can save six months, three microservices, two Kubernetes clusters and several meetings about cloud costs.
After years of working across frontend, backend, APIs, databases, infrastructure, integrations, deployments and production systems, I have learnt that the challenge is rarely just making a feature work.
The real challenge is making it work:
- securely;
- under load;
- when a dependency is unavailable;
- without breaking existing behaviour;
- with logs that are genuinely useful;
- and in a way that another developer can understand on a Monday morning.
AI accelerates implementation.
Experience reduces bad decisions.
Together, they are far more valuable than either of them on their own.
The new role of the Senior Developer
The Senior Developer in the AI era will probably write less code manually for certain tasks.
That does not mean doing less work.
It means spending more energy on:
- understanding the real problem;
- defining requirements clearly;
- setting architectural boundaries;
- creating context for AI agents;
- validating technical decisions;
- reviewing generated code;
- designing testing strategies;
- assessing security and privacy;
- controlling technical debt;
- and making sure speed is not confused with progress.
In other words, we are no longer only authors of code.
We are also becoming orchestrators of systems, context and agents.
The prompt becomes part of the engineering process.
The specification becomes more important.
The architecture becomes the guardrail.
And git diff remains mandatory, because trust is important, but code review is important too.
“But the AI built everything by itself”
A five-minute demonstration can create the impression that AI has built an entire product on its own.
Usually, it has built:
- an attractive interface;
- a few routes;
- a database;
- authentication;
- and at least one API key exposed in the frontend.
Turning that into a reliable product requires decisions that do not appear in the demonstration video.
You still need to think about authorisation, rate limiting, migrations, failure recovery, auditing, accessibility, monitoring, costs, dependencies, licences, backups and maintenance.
The “Generate App” button may generate the application.
Unfortunately, there is still no “Generate Accountability” button.
I do not want to compete against AI
Returning to the job market after nearly eight years at the same company naturally brings a lot of reflection.
The tools have changed.
The processes have changed.
The speed has changed.
But I do not believe the best approach is to prove that I can write code faster than a machine.
That would be like challenging a calculator to a long division competition.
My goal is to show that I know how to use AI to deliver better software, more quickly and responsibly.
I want to work in environments where AI is treated neither as magic nor as a threat, but as a powerful tool within a solid engineering process.
I do not want to be the developer who ignores AI.
I also do not want to be the developer who accepts every generated change because “the tests passed” — especially when those tests were written by the same AI.
I want to operate somewhere between those two extremes.
The fundamentals are still alive
Even with agents, advanced models and natural-language-driven development, some things remain surprisingly important:
- clear requirements;
- separation of concerns;
- low coupling;
- reliable tests;
- security;
- observability;
- documentation;
- review;
- simplicity;
- and good judgement.
AI does not remove these fundamentals.
It increases the impact of applying them — or ignoring them.
With traditional tools, a poor architectural decision might take weeks to spread across a system.
With AI agents, we can replicate it throughout the entire repository before lunch.
My next chapter
I am beginning a new professional chapter as a Senior Full-Stack Developer at a time when AI is transforming how software is designed and delivered.
I bring with me nearly eight years of context, delivery, incidents, difficult decisions, integrations, legacy systems, deployments and continuous learning.
I also bring curiosity.
I want to explore AI-assisted development, agents, RAG, model evaluation, guardrails, automation and architectures designed for increasingly intelligent systems.
But I want to do so without forgetting one basic lesson:
Software does not only need to be generated. It needs to be understood, operated and maintained.
Perhaps that is the real meaning of vibe architecture.
It is not about allowing AI to choose the entire architecture based on the vibes.
It is about creating an architecture clear enough for humans and agents to work together without turning the repository into an escape room.
AI can help us build the future of software.
But someone still needs to review the pull request.
