Platform Engineer - m/w/d

Requirements

• We bring all leading AI models into one secure, model-agnostic platform and make them usable across entire organizations.
• THE ROLE Platform Engineers at Langdock work on shared backend systems that many product features depend on.
• This includes the AI engine, queues, document processing, integrations, code execution, authentication, and billing.
• WHAT YOU WILL WORK ON Examples of the kind of systems Platform Engineers own: - The AI engine at the core of the platform.
• It handles the prompts users send through Langdock and abstracts over providers such as OpenAI, Anthropic, Google, Azure, Bedrock, Mistral, and open-source models.
• Workflows need to execute reliably across agent steps, conditions, loops, structured-output extraction, human-in-the-loop pauses, and actions across hundreds of integrations.
• Today this means JavaScript in some places, but the direction includes broader execution environments such as Bash.
• TECH STACK - TypeScript across a Turborepo monorepo - Next.js, React, and Tailwind on the front end - Node.js services and workers on the back end - PostgreSQL with Prisma; Redis with BullMQ - A multi-provider model abstraction across OpenAI, Anthropic, Google, Azure, Bedrock, Mistral, and open-source models - Sandboxed Node.js for code execution - Multi-cloud storage abstraction over AWS S3, Azure File Share, and GCS - Terraform for infrastructure orchestration - Kubernetes for workloads that need
• CI runs lint, tests, and AI review. - We deploy continuously to production. - 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, explicit rollout notes for risky changes, and production verification after release. - The engineer who ships a change owns it in production.
• experience building back-end systems that handle real load: queues, caches, databases, streaming pipelines, distributed schedulers.
• You can talk through failure modes and tradeoffs from systems you have actually run. - Strong in TypeScript and Node.
• experience: Terraform, Kubernetes, cloud deployments, networking, or operating services across AWS, Azure, or GCP. - Very driven.
• You have high standards for yourself, move fast without needing to be pushed, and want to do the best work of your career. - Working knowledge of the LLM ecosystem at a technical level: context windows, tool calling, streaming protocols, provider quirks, prompt caching. - Habit of adding metrics, traces, and structured logs because you have been on call before and know you will need them. - Security-first mindset.