An Unexpected Yet Natural Journey
When we meet Josefin Ahnlund at Consid’s Malmö office, it’s a sunny but windy morning. In the airy meeting room, she has just filled her coffee cup before settling into one of the chairs. With a warm smile, she begins to tell us about her journey to Consid.
Josefin’s path into the tech world has been far from linear. With a background in the humanities and music, it wasn’t until years of exploration that she eventually found herself studying Engineering Physics at KTH, specialising in Machine Learning. Since then, her career has revolved around data, language, and intelligent systems.
– What attracted me to machine learning was that it demands both technical knowledge and business insight—which suits me perfectly, Josefin says enthusiastically.
She has worked as a Data Scientist in the startup world, consulted for companies like Filmstaden and Apple, and focused on customer service data at IKEA.
– Throughout both my studies and professional life, NLP has been a recurring theme. It’s come naturally since language has always been one of my biggest interests, she explains.
Having spent several years in the industry, Josefin has closely followed the rapid development of the field—from the first basic models to today’s revolution driven by Large Language Models.
– There’s now an AI hype unlike anything I’ve experienced before. It’s both exciting and transformative, but also challenging. I often find that the understanding of the technology doesn’t quite match the hype surrounding it, which can make application and value creation difficult, she reflects.
Programming One of the World’s Most Well-Known Voice Assistants
For a time, Josefin worked with one of the world’s most recognisable voice assistants. It was a complex assignment that involved integrating several technologies into a large-scale production pipeline—from transcribing speech to text, to understanding user intent and generating relevant responses.
– Working with voice assistants means getting multiple systems to operate seamlessly together. It requires both precision and a high-level overview. It was both challenging and incredibly educational, Josefin says.
It was also during this time that she gained a deep understanding of production-level AI solutions and realised that true innovation happens when technology meets real user needs.