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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