Platform Engineer - m​/w​/d

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 6,000 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.

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.

The job is to make these systems reliable and understandable enough that other engineers can build on them. That means designing clear APIs, choosing the right data models, handling failure cases, writing tests for important in variants, adding useful observability, and keeping abstractions simple enough to maintain.

Typical problems include deciding which parts of a long conversation to keep in context, retrying background jobs without running the same side effect twice, handling model-provider failures during streaming responses, refreshing integration tokens before they expire, and enforcing tenant boundaries in shared services.

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. The work includes prompt caching, routing, failover across model deployments, and normalizing provider-specific behavior behind a stable internal interface.
• The workflow runtime. Workflows need to execute reliably across agent steps, conditions, loops, structured-output extraction, human-in-the-loop pauses, and actions across hundreds of integrations. The platform work is about making that execution model predictable, observable, and safe to extend.
• Context-window optimization. Long conversations, uploaded files, and tool calls need to stay correct while model costs stay under control. This is one of the most cost-sensitive parts of the platform.
• A code execution service for running untrusted customer code. Today this means JavaScript in some places, but the direction includes broader execution environments such as Bash. The hard part is clear isolation: secrets, file system access, outbound network access, and tenant boundaries need to be controlled explicitly.
• A flexible integration layer for connecting Langdock to external services. Most integrations are standard REST APIs, but the platform also needs to support industry standards for tools and agents, including Model Context Protocol (MCP) and agent-to-agent (A2A) communication.

You will pick up a platform area over time and can shape where it goes next.

• Type Script 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 portable deployment across cloud providers
• Linear for ticket management
• Datadog and Sentry for observability

You should be familiar with most of this. We trust you to pick up the rest quickly.

• Small temporary squads of 2 to 3 engineers around a topic. Squads form, ship, and dissolve.
• Every change is linked to a Linear ticket and ships via PR. 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. If something breaks, you lead the fix.

• 3 to 6 years of 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 Type Script and Node. Opinionated about API design, abstractions, and where boundaries should sit.
• Some infrastructure experience:
  Terraform, Kubernetes, cloud deployments, networking, or operating services across AWS, Azure, or GCP.
• Very driven. You have…