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Enterprise Customer Success Manager - m/f/d

Langdock
CompanyLangdock
CategoryCustomer Support
LocationBerlin
RemoteOn-site (inferred)
EmploymentNot stated
LevelManager
SalaryEUR 80k–120k
Posted16 Apr 2026
Last verified11 Aug 2026
SourceEmployer ATS (ashby)
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Description
HELP US CHANGE THE WAY THE WORLD WORKS BUILD SOMETHING THAT MATTERS. Langdock exists to change the way the world works, bridging the gap between what technology can do and what people actually do with it. We bring all leading AI models into one secure, model-agnostic platform and make them usable across entire organizations. Over 10,000 companies use our platform every day, from fast-growing startups to some of Europe's largest enterprises. Their employees open Langdock to draft strategies, analyze documents, or automate workflows - helping them to work smarter, think more creatively, and reach their full potential. ABOUT THE ROLE AI is the biggest transformation of our lifetime. Most companies know they need to move. Few know how. This is where you come in. As an Enterprise Customer Success Manager at Langdock, you are the person who guides companies through that shift - as part of our Customer Success organization, working alongside Solutions Engineering. You build the trust that makes companies willing to change. You act as a strategic sparring partner, helping customers navigate the complexity of AI transformation, challenge their assumptions, and make decisions with confidence. You own a portfolio of enterprise accounts: adoption, expansion, retention, and everything in between. Your job is to directly shape how AI lands inside each customer - and how deep it goes. You will work closely with Solutions Engineering to deliver real value and with Product to bring the customer's voice into what we build next. You will have genuine influence over both. And you will use our own product to do your job better than any traditional Customer Success setup could. Everyone at Langdock builds AI agents and workflows to automate their own work. You will too. That is not an add-on. It is part of how we operate. At Langdock, adoption goes beyond seat activation. It is the moment AI becomes how a team works - and our job is to get every customer there. WHAT YOU WILL DO - Own a portfolio of enterprise accounts and be accountable for adoption, retention, and expansion across users, use cases, and deployment scope - Drive measurable AI transformation inside each customer - from first use case to company-wide rollout - Host engaging workshops that inspire and translate directly into new use cases, active users, and visible momentum inside the customer - Navigate complex enterprise stakeholder landscapes: find the champions, manage the blockers, build multi-threaded relationships - Build structured account plans for your highest-value customers: stakeholder maps, health assessments, growth opportunities, risk mitigation - Run strategic customer conversations - QBRs, executive business reviews, and proactive check-ins - that are grounded in data and outcomes - Work cross-functionally with Solutions Engineering, Product, and Sales to translate customer needs into internal action - Build AI agents and automated workflows to manage your own book of business - we expect you to systematically eliminate manual work from your role, not just use AI as a productivity boost - Be the voice of the customer internally without losing sight of Langdock's product direction - our principle is Product > Customer Success > Marketing > Sales YOU MIGHT BE A FIT IF… - You have 5+ years of experience in a customer-facing role with enterprise customers - whether that is Account Management, Customer Success, or a consultative post-sales role. You have worked with real technical products, not just lightweight SaaS tools, and you understand what it takes to drive adoption in complex organizations. - You don't just understand AI - you build with it. You have hands-on experience creating agents, automating workflows, and using AI to do work that would normally require more people. You can talk credibly about model selection, data privacy, and integration architectures with technical stakeholders because you've dealt with