I live in a Balkan country in Europe and have about four years of professional experience across frontend and backend development. My work has included rebuilding websites from Figma designs, building APIs, integrating booking and availability systems, connecting third-party services, supporting payment providers such as Stripe, working with databases and performance optimization, and handling Docker, CI/CD, deployments, logs, configuration, and production incidents. I am not a DevOps specialist, but I would like to develop further in infrastructure and backend engineering.
I do not have a degree, and I am looking for a fully remote web development role in Europe or within European working hours. I am open to frontend, backend, or full-stack work, and possibly moving toward backend or infrastructure over time.
I want to build a portfolio project, but I do not want to spend five months creating another basic CRUD app, to-do list, or clone. I was considering a focused backend-heavy project involving authentication, database design, payments, queues, caching, testing, CI/CD, and observability. However, I am unsure how much employers actually value large portfolio projects.
I am also trying to understand what matters most when applying: the CV, GitHub, projects, system design, algorithms, open source, certifications, or something else. I would appreciate advice on how much not having a degree matters after four years of experience, what fundamentals someone at this level should know, and whether it is better to remain full-stack or specialize more deeply in backend and infrastructure.
I am also unsure how to present AI usage. Is it worth listing tools such as Claude, ChatGPT, or coding agents on a CV? Would using AI heavily to implement a portfolio project be viewed negatively if I designed the architecture, reviewed the code, tested it, debugged it, and understood how everything worked? Finally, if you had six months to become substantially more employable, would you prioritize backend fundamentals, DevOps, cloud, system design, algorithms, projects, interview preparation, or something else?
3 Answers
Do not just list technologies such as SQL, caching, Docker, or CI/CD. Rewrite your work around outcomes: for example, how you reduced a slow query, improved booking reliability, handled payment failures, or resolved a production incident. Even approximate but honest measurements are more persuasive than a long skills list.
A concise case study for two or three real projects can be more useful than an enormous GitHub repository. Explain the problem, constraints, design, decisions, failures, and result. That gives interviewers evidence that you understand systems rather than merely recognizing technology names.
After four years, not having a degree is usually less important if your experience is clearly demonstrated, though some employers and immigration processes may still require one.
Your production experience is probably more valuable than another generic portfolio project. Payments, booking flows, third-party integrations, synchronization problems, and production debugging are strong differentiators, so present yourself around those strengths instead of as a generic full-stack developer.
If you build something, make it small but technically meaningful. A booking service that prevents double bookings under concurrent requests would work well. You could also build a reconciliation service for failed payment webhooks that compares provider events with your database, retries safely, records idempotency, and alerts when states disagree. That demonstrates database design, queues, reliability, edge cases, observability, and business judgment without requiring months of work.
The project matters less than whether you can explain the tradeoffs and show that it solves a realistic problem.
AI tools generally do not need to appear as standalone CV skills. Most developers are expected to use them, and simply naming a coding assistant does not provide much signal. It is better to describe a concrete result, such as adding an AI-assisted support classification feature with validation, tests, monitoring, and a safe fallback.
Using AI to build a portfolio project is fine if you can explain every important part and defend the design. Interviewers may probe the generated sections, so blindly accepting code is the real risk—not the fact that an AI tool helped write it. Treat it as an accelerator, review its output critically, and make sure the final project reflects your own decisions and understanding.
For the next six months, I would focus on a targeted project, core backend and SQL knowledge, HTTP and networking fundamentals, security, testing, concurrency, Docker, deployment, and practical system design. Apply while doing that rather than waiting until you feel completely prepared.
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