Eine zielgenaue Bewerbung für diesen Job — ein maßgeschneiderter Lebenslauf und ein Anschreiben, die genau zur Stellenanzeige passen.
Vestigas is seeking a Team Lead - AI Engineering to own the AI infrastructure for our supply chain platform. You will lead a team of AI engineers, set technical direction, and ensure robust, scalable AI systems in production.
You will guide end-to-end ML pipelines, data studies, and model deployments while aligning with engineering and product leadership. Munich-based hybrid work is offered with ample growth opportunities.
We\'re digitizing the construction industry - are you in?
At VESTIGAS, you build the digital backbone of the construction supply chain. We turn purchase orders, delivery notes, and invoices into a digital, legally compliant platform used in everyday operations by construction companies and suppliers. You\'ll work on a product with real market traction in the DACH mid-market, ship iteratively, and take ownership of solutions that must work in the field- not just in theory.
As Team Lead - AI Engineering, you\'ll own the AI infrastructure and capabilities that power our supply chain platform end-to-end - from pipeline design to production deployment. You bring prior leadership experience and are ready to lead a team of AI engineers: setting technical direction, growing your team\'s capabilities, and making sure the team consistently delivers reliable, scalable AI systems.
Your team will tackle problems like extracting structured data reliably from many different documents, building uncertainty estimation so the system knows when to flag a document for human review instead of silently guessing, and automatically reconciling purchase orders, delivery notes, and invoices against each other - all within legal and compliance requirements that leave no room for \"close enough.\" This is a domain where clean benchmark metrics mean far less than robustness against real documents nobody has seen before - and you\'ll set the technical direction for how the team navigates that trade-off, deciding where to push automation further and where human review still matters.