Full Stack Developer

EPAM·Sweden·Офис·2д. назад

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