Backend Engineer Typescript

pydantic, and properly configure queues, caching, and data stores. - Engineer the RAG pipeline for high-signal retrieval (chunking, hybrid search, re-ranking, caching) and prove the grounding holds. - Build the automated audit and evaluation pipeline, patient simulators for testing agents at scale, and the retrieval layer. - Improve the RAG pipeline with hybrid retrieval and re-ranking, and validate gains with faithfulness and context metrics. - Serve as a backend-first engineer, owning the software underneath the agents and ensuring reliability, safety, and performance. Qualifications - At least 2 years of demonstrable experience building or scaling user-facing AI software that real users touch. - Expertise in Python with strong FastAPI, asyncio, and pydantic skills, and production observability. - Proficiency with GCP services such as Cloud Run or GKE, Pub/Sub, Vertex AI, GCS, Secret Manager, Cloud Logging, and Cloud Trace. - Familiarity with healthcare data and the, ability to

build a patient simulator that lets the team stress-test agents at scale before they reach a real call, Experience with Redis, Postgres, vector stores, Docker, Kubernetes, Terraform, Argo CD, OpenTelemetry, TypeScript, and React is a plus. J-18808-Ljbffr

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 customise and integrates seamlessly into workflows, delivering precise assistance across the care spectrum. Responsibilities - Build and run Cortex, the core AI architecture behind Q, and the services that sit on top of it: automated AI audits, patient simulators, retrieval (RAG), and escalation agents that take over in red-guardrail situations. - 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. - Write fast, well-tested Python services with FastAPI, asyncio, and