Overview
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. Your Role You'll help build the AI infrastructure and capabilities that power our supply chain platform, working across the full lifecycle – from pipeline design to production deployment. With 5+ years of experience under your belt, you're ready for the next step in your career. Maybe you've already mentored or technically guided colleagues, and you're looking to grow steadily into a Team Lead role as you take on more ownership and, over time, people responsibility. The Technical Challenge You'll work on 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 you've never seen before, and where every accuracy gain has a direct, visible impact on our customers' daily operations. Your Mission Lead MLOps processes and maintain infrastructure to keep our AI systems reliable, scalable, and secure Design and implement advanced AI features, working closely with engineering and product teams to deploy machine learning models and data pipelines Improve the accuracy, efficiency, and uncertainty estimation of existing AI features through rigorous testing Contribute to the scalability, mainta
Full job description
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. Your Role You'll help build the AI infrastructure and capabilities that power our supply chain platform, working across the full lifecycle – from pipeline design to production deployment. With 5+ years of experience under your belt, you're ready for the next step in your career. Maybe you've already mentored or technically guided colleagues, and you're looking to grow steadily into a Team Lead role as you take on more ownership and, over time, people responsibility. The Technical Challenge You'll work on 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 you've never seen before, and where every accuracy gain has a direct, visible impact on our customers' daily operations. Your Mission Lead MLOps processes and maintain infrastructure to keep our AI systems reliable, scalable, and secure Design and implement advanced AI features, working closely with engineering and product teams to deploy machine learning models and data pipelines Improve the accuracy, efficiency, and uncertainty estimation of existing AI features through rigorous testing Contribute to the scalability, mainta
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Verification notes
Atlas location:city; evidence: München (Hybrid)
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