Backend Engineer – Platform APIs

شرح موقعیت

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 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. 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.