Fullstack Engineer

1. Full-stack engineering depth (required)
You are not only a prompt author: you ship full-stack software and can own thin slices end-to-end when the prototype must be real.
End-to-end feature development: backend services and SPAs; comfort moving across the stack for prototypes and v1 implementations.
Principal/Senior level at Backend OR Frontend (if you are good at Backend, FE can be intermediate, and in contrast)
Backend: hands-on skills in Java or Golang; RESTful APIs; sound approach to microservices and backward compatibility where relevant; performance, concurrency, and clean structure in code.
Frontend: solid experience with modern frameworks such as [protected info], [protected info], [protected info], [protected info], or [protected info]; strong JavaScript, TypeScript, HTML, and CSS; awareness of testing (e.g. Jest/Mocha) and quality (linting, reviews).
Data: strong SQL skills and query tuning; ACID-aware design for transactional behaviour in prototypes.
Delivery: CI/CD (e.g. GitHub Actions, CircleCI, or similar), Git workflows, code review habits; AWS and Docker for deployable prototypes; Kubernetes exposure is a plus.
Reliability and security in scope of the prototype: authentication/authorization patterns, JWT/OAuth2 at a level appropriate to demos and handover notes; SLO/SLI thinking and observability hooks where the prototype will graduate.
2. Required experience and skills (must have)
Generative AI: Enthusiastic, daily use of generative AI and advanced AI tooling to streamline work and materially accelerate delivery, combined with deep practical understanding of LLMs and proven application in product or internal delivery (not toy prompts only).
Product mindset: Proven track record translating high-level product requirements into detailed requirements and comprehensive technical requirements through close partnership with Product Managers and domain stakeholders.
Communication: Strong verbal and written English for clarity and alignment in distributed, multinational product engineering teams.
Prototyping and product iteration using AI tools and no-code/low-code, grounded in user and operational workflow understanding.
Comfort with ambiguity: ship "something that works first," then refine in tight loops (agile execution).
Track record as a central technical contributor on product initiatives that reached users and business outcomes.
Agile familiarity (Scrum/Kanban); strong problem-solving across technical and product uncertainty.
3. Preferred (nice to have)
Micro-frontends, state machines, advanced Golang or Java concurrency.
Kubernetes, Kafka/RabbitMQ, Prometheus/Grafana/ELK or similar.
DDD, Clean/Hexagonal architecture; experience in accounting, finance, or other domain-heavy B2B products.
Open-source or strong continuous learning profile.