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BLP seeks an AI Automation Specialist to drive automation for enterprise clients across the DACH region. You will tune agents, run post-go-live sprints, and resolve data or process questions with customers, building trust through concrete automation outcomes.
You bring up to 2 years of experience, fluent German and English, and a strong foundation in data, ERP, and process improvement. A hybrid work model includes travel to Zurich and customer sites.
Join BLP and help make the world’s business processes autonomous!
Our agentic platform helps finance and operations teams worldwide remove manual work from their core business processes, so work runs end-to-end with greater control and significant productivity gains.
Today, over 550 enterprise customers use BLP across 1,300+ deployed projects. In the processes we automate, customers achieve productivity improvements of 5 to 10 times.
To date, BLP remains majority employee-owned, with Goldman Sachs holding a minority stake as a growth investor. Our success is driven by deep expertise in technology and business processes, a superior product with strong product-market fit, reflected in an expanding customer base that includes Fortune 500 companies.
BLP is one of Switzerland's fastest-growing SaaS scale-ups; we are now entering our next phase of growth. From our headquarters on Zürich's Bahnhofstrasse, we're expanding globally, with a growing presence across Europe and North America.
Join us and help make autonomous operations the new standard.
This is a full-time position based in Munich or Cologne. Following a comprehensive onboarding period at our HQ in central Zurich (minimum one month), you will work from your home location, with regular visits to customers across the DACH region.
Team collaboration matters a lot to us. You will regularly spend time with the team in person, in Zurich and at our hubs, for team days, all-hands and offsites. These visits are financially supported by blp.
Moves into Project Management or Customer Success Engineering are open at every level, and many of our people make them.
For a process to run autonomously, four things have to line up: the technology, the process itself, the data behind it, and the people who work with it. That alignment does not happen at go-live. It happens in the months after, and that is where our Consulting team sits.
Our customers go live with a working solution. What they do not have on day one is an organization that operates in an AI-native way. Processes were designed around people checking things. Data was maintained well enough for humans to interpret. Teams know the old workarounds better than the new system. Our job is to lead that transition, from a process that is automated in parts to a process that genuinely runs on its own.
This is also where our AI meets the real world, and the real world is messy. When a customer goes live, our agents start processing their documents: invoices, order confirmations, delivery notes, in dozens of formats from thousands of suppliers, with layouts nobody has seen before. Most of it works immediately. The rest is where we come in.
A concrete example. Our AI learns a customer's business logic from their own history. By reading how they have accounted invoices in the past, blp infers the rules they have been applying and makes them explicit. Sometimes it cannot close the loop on its own, because the same kind of invoice was historically booked in two or three different ways and there is no consistent rule to find. In those cases blp asks. It needs the answer once, and every future case follows it. Making sure that answer arrives, and that it is the right one, is our job. Done well, each of those conversations builds trust and removes a category of manual work permanently.
You are hands-on in the automation itself. You configure and tune the agents so they work for a specific customer, dig into exceptions until you find the actual cause rather than patching the symptom, and run automation sprints: the push after each go-live to the target automation rate, and focused sprints our customers book to take their automation further.
You are in front of customers from early on: running the sessions that explain what a new capability does, taking the open questions our AI raises to the person who can decide them, and chasing down why automation is not landing, whether that is a master data problem, a habit, or a process rule that no longer makes sense. You use data-driven insights to decide which conversations to have with customers and which improvements to prioritize.
You work alongside a Consultant on your accounts and build process and platform depth on real customers from the first month.
The following key attributes ensure success in this team: