Key Skills

Job Description

Role: AI Engineer -RAG Platform Location : Utrecht Netherlands (2-3days/week onsite Preferred) Type :Contract/Permanent Important: Please clearly indicate in the motivation field whether the candidate has any planned vacation or special leave during the duration of this assignment; Language: English mandatory; Job description Want to improve one of Client's most strategic AI platforms through data-driven experimentation and measurable model improvements? You & your role As an AI Engineer - RAG Platform, you are responsible for continuously improving the quality, accuracy and effectiveness of Client's internal Retrieval-Augmented Generation (RAG) platform. You work at the intersection of Information Retrieval, Data Science and Software Engineering. Rather than building AI applications or platform infrastructure, your primary responsibility is improving the performance of the RAG system itself. The RAG platform serves thousands of users across Client and acts as a foundation for multiple AI-powered products. Your focus is understanding where and why the system underperforms, designing experiments to validate improvements and translating insights into measurable production outcomes. You contribute across the entire improvement lifecycle, from failure analysis and hypothesis generation to evaluation, implementation and monitoring. You leverage user feedback, retrieval metrics, evaluation datasets and production telemetry to continuously increase answer quality and user trust. This role is designed for a highly analytical and hands-on specialist who wants to deepen their expertise in retrieval systems, evaluation methodologies and applied AI rather than moving towards people management or generic product development. You will work closely with Software Engineers, Platform Engineers, Data Scientists and Product Managers, while acting as the driving force behind the continuous improvement of Client's RAG capabilities. Examples of what you will work on What kind of challenges could land on your sprint board? Investigating why certain user questions produce lower-quality answers and identifying the underlying retrieval or grounding issues; Improving our automated evaluation framework to measure retrieval quality, answer quality and user satisfaction; Running large-scale experiments to determine the impact of different chunking, embedding and retrieval strategies; Analyzing user interactions and system performance data in Databricks to uncover improvement opportunities; Building feedback loops that continuously identify weak responses and feed insights back into the development process; Defining and monitoring quality metrics that ensure every release results in measurable model improvements. Facts & figures What gives this role its reach and relevance? 36 hours per week; A strategic AI platform used across Client; Working on a production AI system where quality improvements directly impact thousands of users; Part of a multidisciplinary team combining engineering, product and AI expertise; Key responsibilities Drive continuous RAG model improvement Improve retrieval performance, answer quality and overall user experience through experimentation, analysis and optimization. Build evaluation and measurement capabilities Develop automated frameworks that measure RAG quality, identify regressions and provide actionable insights for future improvements. Turn insights into production outcomes Translate data analysis and experimentation results into scalable, maintainable engineering solutions that deliver measurable business value. Together we achieve more than alone The RAG platform is not a research project. It is a production product that must continuously improve. You will join a team of experienced engineers who are building and operating critical AI capabilities within Client. Success in this role comes from combining scientific rigor with engineering excellence. We value people who challenge assumptions, validate ideas through data and continuously raise the quality bar. You & your talent We are looking for someone who enjoys solving hard problems and is driven by measurable outcomes. Someone who is equally comfortable discussing statistical significance, retrieval metrics and system architecture. You take ownership of results rather than activities. You are naturally curious, analytical and passionate about improving complex systems. Mandatory skills Retrieval-Augmented Generation (RAG); Large Language Models (LLMs); Python; Databricks; Evaluation frameworks for GenAI systems; Embedding models; Prompt optimization; Data pipelines and workflow automation; CI/CD practices; Azure. Nice to have Experience with fine-tuning language models; Experience with ranking and retrieval systems; Experience with AI observability and monitoring; Experience working with large-scale production AI platforms; Knowledge of synthetic evaluation datasets and benchmark creation. Knowledge and experience Master's degree or equivalent academic level in Data Science, Artificial Intelligence, Computer Science, Mathematics, Statistics or a related field; At least 3 years of relevant experience in machine learning, applied AI or data science; Demonstrable experience improving production-grade RAG systems or advanced retrieval-based AI solutions; Experience designing evaluation methodologies and quality metrics; Strong track record of converting analyses into automated production solutions; Experience working with large datasets and complex distributed environments; Ability to communicate findings and recommendations to both technical and non-technical stakeholders; Proficiency in English, both written and spoken. Additional Information: All departments are working hybrid, in terms of Client @anywhere policy; Due to the congestion at the parking lot Client location Croeselaan in Utrecht, a new parking policy will be introduced as of April 2026. From Monday through Thursday, external staff will no longer be able to park at the Croeselaan. Parking for external staff will remain permitted on Fridays only; Please note: midlance constructions are not allowed according to Client policy. Do not offer candidates via this construction. This will be investigated during the screening; Suppliers must be aware of the laws and regulations regarding employment conditions and Client’s Collective Labour Agreement. This assignment is placed in scale 9; We would like to receive the personal motivation of the candidate and CV in English; A candidate should be submitted exclusively to Magnit – Client during the exclusivity period of 4 business days on one request; Furthermore, the candidate has to be available throughout the entire duration of the assignment; All submitted candidates must be in possession of a valid Passport or ID card, which must be taken along to the interview and at the start of the assignment; Pre-employment screening: If the candidate is selected to start, a pre-employment screening will be executed. As of July 1, 2026, a new PES regulation applies. The PES is outsourced to an external party. More information can be found in Annex A. Your candidate is only allowed to start after the pre-employment screening has been completed successfully; When offering an independent contractor on an ZZP indirect basis, the contract will be handled via a fee-only agreement. This agreement can be requested in advance; By submitting a candidate, you are agreeing to the terms of this specific client. If you are not familiar with these terms, you can ask our recruiters for a copy. IMPORTANT!: Complete all candidate details correctly. Enter the candidate's contact details under ‘email address and telephone number’! The same applies to the address. This is information the client needs to receive with the submission. Incomplete/incorrect submissions risk being rejected.

Core Responsibilities

Improve the production RAG platform by analyzing failures and user feedback, running experiments, and optimizing retrieval performance, answer quality, and user experience. Build evaluation and monitoring capabilities, then translate findings into scalable production improvements with measurable outcomes.

Requirements

Requires a master's degree or equivalent in a relevant field, at least three years of experience in machine learning, applied AI, or data science, and demonstrable experience improving production-grade RAG or advanced retrieval systems. Candidates should be proficient in English and able to design evaluation methods, work with large datasets, and turn analysis into automated production solutions.