What we’re building: An AI-native product suite that improves operational performance across manufacturing, quality, and supply chain. We’ll start with one wedge use case that proves ROI fast (site-level, measurable), then expand into adjacent workflows on the same data backbone. What you’ll do (day 1 responsibilities): - Own the full technical direction: architecture, stack, delivery standards, and product execution. - Build the first MVP end-to-end: data ingestion → decision logic/models → workflow UI → outcome tracking. - Turn messy enterprise data into usable entities and features (materials, lots, batches, lines, tests, deviations, holds, orders). - Ship decision products, not dashboards: risk scoring, prioritization queues, optimization recommendations with explainability and audit trails. - Set up secure deployment basics for enterprise pilots (single-tenant cloud/VPC option, RBAC, audit logging, encryption, monitoring). - Hire and lead the technical team as we get traction. Who you are: - Senior engineer who has shipped production systems (not just prototypes). - Strong in Python + SQL and comfortable with data engineering and backend fundamentals. - Pragmatic ML and/or optimization experience (tabular ML, forecasting, anomaly detection, heuristics/OR tools). - You think in closed loops: data → decision → workflow → outcome → learning. - Bonus if you’ve touched ERP/MES/QMS/LIMS ecosystems or industrial SaaS. How to apply Send: - LinkedIn/GitHub - 2–3 examples of systems you shipped (what you built, your role, what changed in the real world) - Current location + availability for in-person meetups in Zurich