Full Stack Developer

Full Stack Developers in Goteborg design, build and improve web applications that support real business workflows, including practical AI-enabled features. You own features across frontend, backend and data layers, from requirements clarification through production delivery. Working onsite with product owners, architects, designers and client stakeholders, you combine hands-on engineering with precise specifications, responsible use of AI development tools and validation of business outcomes. This role matters because it drives the creation of robust, user-focused solutions that enhance enterprise operations. Responsibilities Understand how users work, clarify requirements with product owners and domain experts, and surface assumptions, dependencies and constraints before implementation Translate agreed requirements into buildable feature specifications and testable acceptance criteria, covering business rules, data flows, permissions, edge cases and failure scenarios Design, implement and maintain frontend components, backend services, REST APIs and data models within the agreed architecture; explain technical trade-offs and raise decisions that affect other systems Use AI coding assistants and agents to support implementation, testing and refactoring. Provide clear context and constraints, review generated changes and remain accountable for their correctness, security and maintainability Integrate applications with enterprise systems and AI services through APIs, supporting features such as summarization or semantic search with appropriate authentication, data consistency, output validation and fallback behavior Write automated unit, integration and end-to-end tests. Demonstrate features against agreed business scenarios, incorporate user feedback and make validation gaps visible Contribute to CI/CD pipelines and deployment automation; support releases, application monitoring and production troubleshooting Diagnose issues across the stack using logs, traces and test evidence, distinguishing requirement gaps from implementation defects and test failures Improve existing codebases through focused refactoring, peer reviews and documentation; communicate delivery risks and propose practical improvements within the agreed scope Requirements Five years in software development, with frontend and backend experience and independent delivery of production features Strong proficiency in at least one backend stack, such as C#/.NET, Node.js or Python, with sound knowledge of API design and application architecture Solid JavaScript/TypeScript skills and practical experience with React, Angular or a comparable framework, including responsive, accessible interfaces and application state management Experience with SQL and/or NoSQL databases, data modeling, schema changes, query performance and data consistency trade-offs Practical experience with cloud-hosted applications, preferably Azure, and working knowledge of containerization, CI/CD and infrastructure as code Strong automated testing, debugging, Git and code review skills, with experience addressing application security, reliability and performance Practical experience using AI coding assistants or agents, including supplying requirements and repository context, checking generated code, diagnosing failures and following project data-handling rules Practical exposure to integrating LLM APIs through a project or prototype, with a basic understanding of prompts, output validation, data privacy and usage costs Ability to clarify business workflows and turn them into implementable requirements and acceptance scenarios, including behavior inferred from existing applications Clear communication with technical and non-technical stakeholders; willingness to ask questions, challenge unsupported assumptions and escalate risks early Ownership of feature quality through implementation, deployment and user feedback, with effective collaboration across product and engineering Nice to have Experience with retrieval-augmented generation (RAG) or managed cloud AI services, particularly on Azure Experience with identity and access management, event-driven integrations or enterprise application modernization Deeper experience with Kubernetes, infrastructure as code or application observability Experience in automotive or other enterprise environments, and participation in client workshops or user acceptance sessions