AI Engineer (Backend)

As an AI Engineer (Backend), you will take end-to-end ownership of Cortex, the core AI "brain" architecture powering the entire platform^. This is a backend-first role-your mission is to make the AI systems incredibly reliable, ultra-fast, compliant, and deeply observable in production, rather than inventing new ML models from scratch^.

In this high-autonomy role, you will collaborate directly with a tight-knit, best-in-class AI team to solve some of the hardest challenges in the industry: real-time voice AI, automated AI audits, patient simulators, and safety-critical escalation systems in a highly regulated medical environment^., * Scale & Own "Cortex": Architect, deploy, and maintain robust, modular backend systems using clean architectural principles (SOLID, Clean/Hexagonal Architecture, DDD)^. Manage service boundaries, reliability targets, and proactively mitigate failure modes^.

• Orchestrate Multi-Agent Systems: Wire up complex multi-agent orchestration, managing routing between agents, shared state, and clean tool interfaces^.
• Optimize the RAG Pipeline: Engineer high-signal retrieval layers (chunking, hybrid search, re-ranking, caching) and relentlessly prove that grounding holds^.
• Build Platform Infrastructure: Develop automated audit/eval pipelines and patient simulators to stress-test agents at scale before they ever hit production^.
• Master Production Engineering: Write lightning-fast, well-tested Python services utilizing FastAPI, asyncio, and pydantic while optimizing queues, caching, and data stores^.
• Drive Deep Observability: Implement OTEL-first tracing across the agent graph, tracking cost, latency, token visibility, and configuring CI gates to catch regressions before they ship^., * Meaningful Impact: You aren't just building a generic chatbot; you are engineering safety-critical systems that actively improve how healthcare is delivered^.
• High-Level Ownership: As an early hire, you will shape the technical foundation of a fast-growing startup and tackle genuinely difficult engineering hurdles^.
• Flexible Setup: Enjoy a European remote-first model, backed by occasional visits to a vibrant, high-energy office hub in Barcelona^.
• Flat Hierarchy: Work closely alongside clinical experts and elite engineering talent in an environment free of corporate bureaucracy^.

• Backend Core: 3-5+ years of software engineering experience with expert-level Python, FastAPI, asyncio, and pydantic^. You care deeply about how your code behaves in production^.
• Proven AI Track Record: At least 2+ years of demonstrable experience building, scaling, and taking user-facing AI/LLM applications from MVP (0) to production (1)^.
• Architectural Depth: Deep understanding of architectural design patterns (SOLID, Event-Driven, DDD) to manage complex system boundaries^.
• Startup DNA: You thrive in high-intensity, fast-moving environments and know what wearing several hats actually costs^.
• Autonomy: A self-starter who excels with minimal supervision-moving easily between managing agent patterns, eval-driven development, and production software^.

Nice to Have

• Real-Time & Voice: Experience with WebRTC, LiveKit, SIP, VAD, barge-in, or turn-taking^.
• Advanced LLM Tooling: Programmatic prompt optimization techniques and LLM-as-judge evaluation setups^.
• Cloud & DevOps: Familiarity with GCP (Cloud Run, GKE, Pub/Sub, Vertex AI, Cloud Logging/Trace) and infrastructure tools like Terraform or ArgoCD^.
• Industry Savvy: Prior experience handling healthcare data or working within highly regulated/compliant frameworks^.

Example Problems You'll Tackle

• Stand up the AI audit pipeline so evals run automatically on slices of production traffic, with regression gates wired into CI^.

You will be joining a high-growth Health-Technology venture based in Barcelona that has just hit a major inflection point^. They are the innovators behind a cutting-edge AI assistant that acts as a digital partner for clinical teams, recently going live with major health systems across Europe^. They are scaling rapidly to meet significant healthcare contracts, moving from MVP to large-scale production^. This is an international team of clinical experts and elite engineers who believe AI should solve mission-critical medical problems in real-time^. If you want to build the technical foundation of a future leader in healthcare AI, this is your moment^.