AI Engineer Backend (Remote - ES/UK only) in London

About Us

Quadrivia is the health technology company behind Q, a comprehensive, controllable, and customizable assistant AI built by clinicians, for clinicians.

Addressing the urgent shortage of healthcare professionals, Q provides real-time, personal, and reliable support for clinical tasks across the care continuum.

Designed for providers, payers, and pharmaceutical companies, Q is easy to customize and integrates seamlessly into workflows, delivering precise assistance across the care spectrum.

About Us

Quadrivia is the health technology company behind Q, a comprehensive, controllable, and customizable assistant AI built by clinicians, for clinicians.

Addressing the urgent shortage of healthcare professionals, Q provides real-time, personal, and reliable support for clinical tasks across the care continuum.

Designed for providers, payers, and pharmaceutical companies, Q is easy to customize and integrates seamlessly into workflows, delivering precise assistance across the care spectrum.

The Role

• You’ll build and run
• Cortex

, the core AI architecture behind Qu, and the services that sit on top of it: automated AI audits, patient simulators, retrieval (RAG), and the escalation agents that take over in red‑guardrail situations.

This is a backend role first.

The job is to make our AI systems reliable, fast, and observable in production, not to invent new ML.

You own the software underneath the agents.

We’re a small team that ships real systems.

We’ve built our entire AI‑driven evaluation system, our voice orchestrator, and a multi‑hierarchical RAG platform from scratch.

• What You’ll Do
• Design and maintain robust, modular backend systems using clean architectural (SOLID) principles to ensure long‑term maintainability, scalability and flexibility as the agentic stack evolves.
• Own Cortex end‑to‑end: architecture, API design, service boundaries, reliability targets, and proactively managing failure modes.
• Build the platform services around it. The automated audit and eval pipeline, patient simulators for testing agents at scale, and the retrieval layer.
• Write fast, well‑tested Python services with Fast API, asyncio, and pydantic, and get the queues, caching, and data stores right.
• Wire up the multi‑agent orchestration: routing between agents, shared state, and clean tool interfaces.
• Engineer the RAG pipeline for high‑signal retrieval (chunking, hybrid search, re‑ranking, caching) and prove the grounding holds.
• Make the whole thing observable: structured logs, OTEL tracing across the agent graph, cost, latency and token visibility, dashboards, and CI gates that catch regressions before they ship.
• Minimum Qualifications
• Your core is backend and software engineering. You write clean, maintainable services and you care how they behave in production.
• Deep understanding of architectural design patterns (e. g., Clean/Hexagonal Architecture, Domain‑Driven Design, SOLID, event‑driven) to manage complex system boundaries.
• At least 2 years, demonstrable, building or scaling user‑facing AI software that real users touched. We’ll want to see it.
• Expert Python, with strong Fast API, asyncio, pydantic, and production observability.
• Comfortable with agent patterns and eval‑driven development.
• You’ve worked at a startup before and know what wearing several hats actually costs.
• Nice to Have
• Real‑time and voice: Web RTC, Live Kit, SIP, VAD, barge‑in, turn‑taking. Useful here, not required.
• Programmatic prompt optimization techniques.
• LLM‑as‑judge setups and other evaluation tooling.
• GCP: Cloud Run or GKE, Pub/Sub, Vertex AI, GCS, Secret Manager, Cloud Logging and Trace.
• Healthcare data familiarity.
• 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.
• Build a patient simulator that lets us stress‑test agents at scale before they ever reach a real call.
• Improve the RAG pipeline with hybrid retrieval and re‑ranking, then prove the gains with faithfulness and context metrics.
• Get OTEL‑first tracing across the agent graph, with automated eval triggers on live traffic.
• Turn EHR integrations into reliable tools the agents can call.
• Tech Stack

Python, Fast API, pydantic, asyncio, Redis, Postgres, vector stores, Docker, Kubernetes, Terraform, Argo CD, OTEL, Type Script, React.

Real‑time stacks (Web RTC, Live Kit, SIP, STT/TTS) where the work touches voice.

• What Success Looks Like
• Quadrivia’s backend becomes a reference for reliability, safety, and performance.
• Your services run above 99.99% availability under strict regulatory constraints.
• Other engineers build new clinical workflows and agent capabilities quickly and safely.
• AI‑generated code gets reviewed, corrected, and owned by you.
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