Founding Principal Backend Engineer

## Responsibilities - Build the core system of the platform: APIs, tenancy, authentication, RBAC, async jobs, uploads, storage logic, audit logs, service contracts, and the AI control plane. - Develop the V1 of the AI control plane: tenant isolation, workspaces, permissions, RBAC, and real logical isolation. - Create critical APIs for the platform with versioning, error handling, pagination, streaming, and progressive states where applicable. - Build the async job layer with retries, idempotency, cancellation, prioritization, and backpressure, designed for real load, not demos. - Implement audit logs and traceability from the first commit, not as a later patch. - Define internal contracts that allow AI workflows, RAG, agents, and model routing to operate with observability, limits, and ownership clarity. - Develop usage metrics, tenant limits, cost tracking, and rate limits — the operational foundation for a governable platform. - Make build vs buy decisions with the founding team and mentor on backend standards as the team grows. ## Requirements - Proven track record building and scaling SaaS backends in production — from 0 to 1 or from 1 to n. Products sensitive, multi-tenant, or high operational criticality. - Experience with real permissions, workspaces, tenants, and logical isolation, not just read from a blog post. - Solid design of async jobs: retries, idempotency, backpressure, traceability. If you've lived the edge cases, even better. - Strong judgment in data modeling, API versioning, and service contracts. - Ability to design audit logs and traceability for products where evidence matters. - Understanding of how to record and govern model calls, costs, latencies, and outputs — even if you're not an ML specialist. - Solid backend experience in Python and/or Go; advanced Postgres; real experience with observability and debugging in production. - High English and operational Spanish, or real ability to work presentially in Madrid in a bilingual team. ## Nice to Have - Experience with AI gateways, model routing, RAG pipelines, or LLM application backends. - Experience with OpenTelemetry, Temporal, Redis/BullMQ, SQS, Kafka/NATS, pgvector, or equivalent tools. The important thing is not having used exactly each one, but having built equivalent systems in production. - Background in fintech, healthtech, legaltech, cybersecurity, data platforms, B2B SaaS enterprise, or products with sensitive data. - Experience in regulated environments or with compliance and audit requirements. ## Benefits - Be part of a well-funded European Deep Tech startup with a strong founding team in Madrid. - Define the architecture, code, and technical culture that the company will inherit for years. - Work on a product that is foundational and critical for clients operating in high operational and decisional contexts. - Collaborate with a team that values speed of iteration and quality of technical decisions in a shared whiteboard environment. https://jobs.ashbyhq.com/naiian/dae21143-7e05-4d56-9126-75859542085 Sobre Naiian Naiian is a European Deep Tech startup with a team in Madrid, well-funded, and with a founding team with experience in product, applied AI, and engineering in critical environments. We build for clients operating in high operational and decisional contexts, where auditability, integration with verifiable sources, and human approval mechanisms for sensitive tasks are not features — they are the base. El rol y ¿por qué existe? You will build the nervous system of the platform: APIs, tenancy, authentication, RBAC, async jobs, uploads, storage logic, audit logs, service contracts, and the base of the AI control plane. The reason this role exists is concrete: when usage increases, when the load concentrates in critical windows, or when AI-assisted workflows start to operate with traceability, permissions, costs, and evidence, the platform cannot become chaotic. The layer you build will determine if that happens or not. You will also leave the first version of the AI control plane ready: the layer that records, governs, and orders how models, workflows, RAG, outputs, costs, latencies, and permissions are used. You don't need to be an expert in machine learning, but you do need to understand that model calls, AI jobs, events, and evidence cannot be scattered or without traceability.