A major insurance organisation is extending its Life digital portfolio with practical AI use cases for business operations. The senior AI Solutions Analyst & Engineer will translate operational needs into GenAI and LLM solutions, including an enterprise-grade conversational AI chatbot, combining business analysis, prompt engineering, RAG pipelines, and evaluation frameworks.
The mission
The Life Digital Solutions team owns digital initiatives for the Life business line and is establishing a roadmap for the adoption, deployment, monitoring, and evaluation of AI solutions. The work connects business stakeholders with IT colleagues and a central AI competence centre covering data, machine learning, architecture, and security. Solutions must reflect operational workflows, user needs, governance requirements, and the constraints of a regulated environment.
You will take GenAI projects from discovery and solution definition through build, testing, rollout, and continuous improvement. Your scope includes capturing requirements, applying prompt engineering and RAG pipelines, translating decisions into an executable backlog, and ensuring that model behaviour can be measured, explained, and improved. You will also contribute to internal awareness through clear documentation, playbooks, training materials, and runbooks.
Your responsibilities
- Shape the roadmap and solution approach for GenAI use cases in collaboration with Life stakeholders, IT, and the central AI competence centre.
- Translate business requirements and operational constraints into functional specifications, user stories, acceptance criteria, and executable backlog items.
- Design and refine LLM workflows, prompts, retrieval patterns, and conversational AI capabilities for an enterprise-grade chatbot.
- Establish LLM testing and evaluation frameworks using golden sets, regression tests, adversarial prompts, and user testing.
- Define quality and operational measures, including groundedness, response quality, latency, cost, prompt and response traces, and other monitoring signals.
- Deliver knowledge transfer and structured reporting so that users and technical teams understand solution behaviour, limitations, and controls.
Your profile
Essential skills
- Bring at least 5 years of experience across business analysis, digital product delivery, or solution engineering, including at least 2 years working with GenAI/LLM-based solutions.
- Demonstrate end-to-end delivery of conversational AI or chatbot solutions, from discovery and build through testing, rollout, and improvement.
- Design prompts, RAG pipelines, and evaluation frameworks, and collaborate effectively with data, ML, architecture, and security specialists.
- Convert business needs into functional specifications and Agile delivery artefacts, including backlog items, user stories, and acceptance criteria.
- Define and execute LLM test strategies covering golden sets, regression testing, safety testing, adversarial prompts, and user acceptance testing.
- Understand operational workflows and constraints, and use structured communication to explain model capabilities, limitations, and risk controls to technical and non-technical audiences.
- Work hands-on with MS Office 365, Copilot M365, Power BI, Azure Databricks and Azure Data Lake, plus Python and SQL.
- Work autonomously in Agile environments while remaining structured, conscientious, flexible, collaborative, and able to manage firm deadlines.
Preferred skills
- Experience in the insurance or financial industry, particularly in a regulated business context.
- Knowledge of data science concepts and practices.
Languages
- Dutch or French: C1 or higher.
- The other national language: B2 or higher.
- English: B2 or higher.
Education
- Master’s degree or equivalent professional experience.