AI Solution Engineer

AI Solution Engineers shape the future of business workflows by designing, building and operating advanced AI capabilities. In Goteborg, onsite collaboration with users, product teams, architects and delivery colleagues anchors technical innovation in practical outcomes. You combine C#/.NET and Azure expertise with applied AI skills to clarify requirements, validate problems and deliver reliable, measurable results. Leveraging precise specifications and AI coding agents, you support robust delivery and continuous improvement. This role matters because it connects technical excellence to real business impact.

Responsibilities

• Work with business stakeholders and users to understand workflows, clarify assumptions, assess data availability, and agree measurable acceptance criteria for AI capabilities
• Turn agreed requirements into buildable specifications covering inputs, outputs, data contracts, business rules, failure paths and validation scenarios
• Design and implement AI services and integrations using C#, .NET, ASP.NET Core and Azure, making technical decisions within agreed architectural constraints
• Integrate models, enterprise data, and APIs into applications; implement retrieval-augmented generation and controlled agent workflows where appropriate, with clear human review and fallback paths
• Build focused prototypes to test feasibility and assumptions, then develop validated capabilities into maintainable production services
• Use AI coding agents against explicit specifications; review generated code, test behavior, diagnose failures and refine specifications or implementations based on evidence
• Define evaluation datasets and quality thresholds; validate output quality, security, latency and cost, and work with users to confirm the capability improves the intended workflow
• Implement CI/CD, automated testing, infrastructure as code, deployment, monitoring, and rollback; manage model and prompt versions and investigate production issues
• Address access control, sensitive data handling, prompt injection and unreliable outputs; document limitations, operating procedures and design decisions
• Explain technical trade-offs and operating costs, surface risks early and provide implementation guidance and code reviews while keeping delivery within the agreed scope

Requirements

• Five years of professional software engineering experience, including two years building applied AI/ML solutions
• Evidence of delivering at least one AI capability into production, with direct responsibility for implementation, deployment, evaluation and operational improvement
• Strong hands-on C#, .NET and ASP.NET Core skills, including API design, asynchronous processing, automated testing, and integration with databases and enterprise systems
• Practical Azure experience covering application hosting, identity, data access and AI services; a solid DevOps foundation in CI/CD, containers and infrastructure automation
• Working knowledge of ML and generative AI fundamentals, with hands-on experience integrating LLM APIs and retrieval-based applications and evaluating their limitations
• Experience defining evaluation criteria and representative test cases, monitoring AI behavior and balancing output quality, latency, reliability, and inference cost
• Ability to clarify ambiguous requirements, model data and workflow states and produce testable specifications; practical experience using AI coding assistants or agents with code review and verification
• Strong troubleshooting and communication skills: able to distinguish requirement gaps, data or model limitations, implementation defects and evaluation issues, and explain findings to technical and business colleagues
• Ability to work independently within an agreed scope and collaborate across product, engineering and operations
• Relevant degree or equivalent practical experience

Nice to have

• Experience with controlled agent workflows, tool integration, human approval mechanisms or business process automation
• Python, model adaptation or fine-tuning and established MLOps practices for custom models
• Experience with Kubernetes, reusable AI components or improving an existing enterprise codebase
• Automotive industry experience or relevant Azure, AI or cloud certifications