Rikard Olsson

Leading the replacement of legacy systems that have reached their practical limits in both performance and product complexity, applying combinatorial optimization, logical modeling, and scalable system design. The transformation is business-critical: without it, the product range simply cannot be sold at the scale the business is aiming for. The new foundation dramatically shortens time-to-market and optimizes throughput across every stage of the pipeline.

Co-designed a novel similarity framework capable of combining heterogeneous data sources—including categorical, numerical, and textual attributes—into a unified similarity metric. Large Language Models (LLMs) formed a fundamental part of the solution, enabling rich semantic understanding of textual product data and allowing it to be integrated with structured attributes in a mathematically consistent way. The work addressed complex challenges related to bias, weighting, representation, and explainability across highly configurable products.

Led the development of a combinatorial optimization service for generating car configurations in Volvo Cars' global commercial configurator, used across all markets worldwide. The service's foundational techniques were derived from my 2019 master's thesis, which I further enhanced after graduation. This advanced solution replaced the legacy system during 2020–2021, delivering improved efficiency and adaptability for the global car configuration platform. The project was a resounding success—removing inefficiencies across the global configuration pipeline and delivering substantial cost savings for Volvo Cars as a company.

M.Sc. Computer Science (Algorithms, Language & Logic)

Chalmers University of Technology, Gothenburg | 2017 – 2019

B.Sc. Software Engineering

Jönköping University | 2014 – 2017