Product Engineer - m/w/d

Requirements

• We bring all leading AI models into one secure, model-agnostic platform and make them usable across entire organizations.
• We want that level of product taste while solving a much more complex AI and enterprise problem.
• Admins need to manage agents at scale: who can create them, who can use them, how they are shared, how they are audited, and how they fit into existing security policies. - Excel and Outlook plugins that bring Langdock into the tools enterprise users already live in. - The Langdock Workflows product, which allows users compose multi-step automations with agents, conditions, loops, structured outputs, and human-in-the-loop nodes.
• TECH STACK - TypeScript across a Turborepo monorepo - Next.js, React, Tailwind on the front end - Node.js, PostgreSQL with Prisma (and pgVector), Redis with BullMQ on the back end - React Native and Expo for the mobile app - Linear for ticket management - Datadog and Sentry for observability You should be familiar with most of this.
• CI runs lint, tests, and AI review.
• One human review is required. - We deploy continously to production. - Engineers have the opportunity to join customers on-site, to sit with real users during hackathons or workshops - We use AI tools heavily in engineering.
• You have freedom in the tools to use (eg. Cursor, Claude, Codex).
• We are building a strong harness that allows engineers to move fast while shipping high quality software. - We are building a strong operating system around AI-assisted shipping: clear ticket context, focused branches, AI review before human review, screenshots or videos for product changes, and production verification after release. - The engineer who ships a change owns it in production.
• experience shipping production software people actually use. - Strong in TypeScript, React, and Node.
• Comfortable with Postgres, Redis, queues, and async work. - Very driven.
• You want to understand why people behave the way they do, and you use that understanding to make product decisions. - Able to take a vague problem to a shipped feature.
• You scope, decide, implement, test, and iterate. - A working understanding of how LLMs behave: context windows, tool calling, streaming, and the differences between major providers. - Heavy user of AI tooling in your own development workflow, with opinions on what works and what does not.
• We are looking for people who are already compounding their output with AI. - Demonstrated ability to ship things end to end: side projects, open source, products with real users, or something equivalent.