Artificial Intelligence Engineer

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. 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