(Senior) Platform Engineer - m/f/d
Build and operate the shared infrastructure that powers Langdock’s AI platform, turning complex capabilities like agent runtimes and model gateways into stable, reusable services for European enterprises.
- Compensation
- €135K–€200K / yr
- Location
- Berlin
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Full time
- Experience
- Not specified
- Category
- Devops
- Posted
Skills
Must have
- You have several years of experience, typically 3 to 6, building and operating backend systems under real load. You are strong in TypeScript and Node.js and comfortable with Kubernetes, Terraform, net
- You have strong platform design judgment and approach foundational work with diligence. You can define stable service boundaries and APIs while accounting for data integrity, tenant isolation, backwar
- Given an underspecified systems problem, you identify the most important invariant, decide where to go deep, and defend what you deliberately leave out. You investigate unfamiliar components and failu
- You treat deployment as the start of ownership. You make systems observable, respond when they fail, and turn incidents into lasting improvements while moving with calm urgency and maintaining a high
- You use AI tools to multiply your output while remaining responsible for the architecture and every change that ships. You share context, ask for input, disagree directly and respectfully, and help ot
Full description
Where Europe's enterprises adopt AI
Langdock is the AI platform used by more than 10,000 companies to give employees secure access to the leading AI models, to build and share agents and to automate repetitive workflows. We have grown past $40M ARR while remaining a small team, and we care deeply about operating efficiently across the entire company.
For many enterprises, Langdock is becoming the place where most of the net-new work is produced. As people and agents create more documents, analyses, decisions, and automations inside AI interfaces, the context and data behind that work accumulate within Langdock. This gives us the opportunity to earn a larger role in their technology stack by building a platform they choose to rely on.
Our ambition is to build that platform for European enterprises while preserving their control over data, model providers, and deployment environments. We have made meaningful progress at the application layer, but much of the foundation beneath it still needs to be built.
You can watch the Meet the engineering team video to get a feeling for how we work.
The role
Platform Engineers own the shared capabilities that Langdock's products are built on. They treat these capabilities as internal products: defining the interfaces other teams build against and remaining accountable for their reliability, evolution, and operation over time.
The work is to turn complex capabilities such as model access, agent execution, authorization, queues, integrations, document processing, and sandboxed compute into stable, reusable building blocks. Each capability needs a clear contract and must be able to evolve without forcing coordinated changes across the rest of the product.
You own a service from design through long-term operation. That includes defining its API and failure behavior, planning migrations, implementing and deploying it, making it observable, and responding when it fails in production. When a service requires a new underlying mechanism, you work with Systems Engineers while remaining accountable for the service boundary and lifecycle.
What you might work on
Current platform work includes:
- A durable agent runtime. The current engine runs model and tool loops for Chat, Workflows, automations, and subagents, but active execution is still owned by one process. We are separating the runtime from product-specific state and designing durable runs that can wait for approvals or external events, recover after a process dies, and continue without repeating committed tool calls. This also includes deterministic context reconstruction, compaction, and prompt-cache efficiency.
- The Model Gateway. We are building one inference boundary for every Langdock product, with a model-agnostic API over managed and customer-provided models. The work includes provider adapters, routing across accounts and regions, rate limits, bounded fallback, streaming safety, usage tracking, and a control plane.