Backend Engineer Platform APIs

We are building an enterprise AI platform in which AI agents operate business applications through their user interface, the way a person does. It runs inside customers own environments, including on-premises and air-gapped sites. We are a small, senior team working in two-week sprints towards a first production release in December 2026.

You will own the platform's core backend:

• the APIs, data model, and permissions everything else is built on
• the audit trail that records every agent action
• the layer that exposes automated workflows to other systems as stable APIs and MCP tools

What you will do

• Design and build the platform's core services in TypeScript (Node.js) and/or Python, and in Go where it fits best.
• Own the GraphQL API and its typed schema, plus REST and streaming interfaces (SSE, WebSockets) where needed.
• Design the core data model and storage: PostgreSQL, Redis, S3-compatible object storage, and graph or vector stores where needed.
• Expose automated workflows to other systems and agents as stable, versioned APIs and MCP tools.
• Build the orchestration layer for long-running agent work: workflow and state-machine engines, queues, scheduling, retries, and idempotency.
• Implement authentication, policy-based authorisation (e.g. Cerbos, OPA), multi-tenancy, and an immutable audit log of every agent action.
• Build services that run the same way in the cloud and in air-gapped on-premises environments, with no managed cloud service in the critical path.
• Add observability (structured logs, metrics, OpenTelemetry tracing) and take part in on-call for the services you own.

What we are looking for

Must have

• 10+ years of professional software engineering, with a strong focus on backend and platform development.
• Strong TypeScript / Node.js (e.g. NestJS, Fastify) and/or Python (e.g. FastAPI).
• Deep experience in API design, especially GraphQL schema design and typed contracts.
• Production experience with PostgreSQL: schema design, indexing, migrations, and query tuning.
• Distributed-systems fundamentals: consistency, idempotency, back-pressure, and failure handling.
• Experience with authorisation models, audit logging, and security fundamentals (OWASP, secrets, least privilege).
• Experience with Docker and deploying services to Kubernetes.
• A track record of technical leadership: owning architecture decisions, mentoring engineers, and raising engineering standards across a team.

Nice to have

• Go for high-performance services.
• Building MCP servers or tool endpoints for AI agents, or integrating LLMs through OpenAI-compatible gateways.
• Workflow and state-machine engines (e.g. Temporal, Trigger.dev, XState), event sourcing, or CQRS.
• Policy engines (Cerbos, OPA).
• Graph databases (e.g. Memgraph, Neo4j) or vector search (pgvector, Qdrant).
• Building software for on-premises, air-gapped, or regulated environments.

What we will assess

• Technical exercise: build a small service with a typed API, persistence, authorisation, and tests.
• System design: design the API, permissions, and audit model for a platform where AI agents act on users behalf.
• Collaboration: working with frontend, full-stack, AI, and DevOps engineers.

Why join

• Own the backbone that lets AI agents act safely in real enterprise and government systems.
• Shape the platform's core architecture from day one.
• A modern, cloud-agnostic stack on Kubernetes, with GitOps delivery.