> Markdown version of [/jobs/ext/3705508-ai-software-engineer-llm-rag](https://www.wearedevelopers.com/jobs/ext/3705508-ai-software-engineer-llm-rag). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Software Engineer - LLM & RAG - **Company:** Pauwels Consulting - **Location:** Schaarbeek, Belgium - **Experience:** Experienced - **Contract:** Temporary contract - **Skills:** Agile Methodology, Artificial Intelligence, Microsoft Azure, Cloud Computing, Software Debugging, Python (Programming Language), TypeScript, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, AI Platforms, Kubernetes, Figma, Terraform - **Published:** October 13, 2026 - **Apply:** https://europa.eu/eures/portal/jv-se/jv-details/NTI0YTExZTEtMWU3Yy00MDZjLTkyYjgtNzUzMzI4YmY0OTY0IDM?lang=en ## About the Role * You have 3+ years of experience in functional analysis, requirements engineering, and process modeling. * You bring technical knowledge of LLM harnesses, agent workflows, RAG, and prompt engineering. * You possess a solid understanding of CI/CD pipelines and cloud infrastructure tools such as Azure, Kubernetes, and Terraform. * You're skilled in designing test scenarios and automated evaluations for model behavior and tool integration. * You have experience inspecting logs and agent execution traces to debug issues related to token usage, context, and latency. * You're proactive and solution-oriented with a proven track record in stimulating user adoption and onboarding. * You are fluent in English. Nice to Haves * Development experience in Python and TypeScript. * Knowledge of Figma and design thinking practices. * Experience with Targetprocess or similar Agile project management platforms. * Familiarity with digital user experience, web design, or marketing content design. ## Description * Develop and validate AI platform functionality using LLM harnesses and agent workflows. * Translate business needs into user stories, functional requirements, and acceptance criteria. * Onboard teams to new AI capabilities through workshops, demonstrations, and hands-on sessions. * Measure and collect user feedback to drive continuous improvement of the platform. * Assess internal and external AI capabilities to provide recommendations for future enhancements. * Maintain clear documentation ensuring traceability from business requirements to technical implementation.