Mia Qi

Led full-stack development for an internal enterprise system supporting multi-location veterinary clinic operations (appointments, medical records, prescriptions, inventory, and billing) across 20+ clinic locations. Architected a cloud-native microservices platform using React/TypeScript, Node.js, AWS ECS/Fargate, Lambda, API Gateway, Docker, and Terraform, cutting new-clinic onboarding effort by 35%. Built a GraphQL BFF layer aggregating clinic/pharmacy/inventory/customer-portal data behind Kafka event streams and Redis caching, reducing cross-system lookup time by 42%. Orchestrated workflow state across appointment/visit/prescription/billing/fulfillment using Kafka domain events, reducing manual status reconciliation by 40%. Integrated an LLM API + RAG workflow for clinic operations documentation, improving cross-team support resolution speed by 20%. Modernized appointment scheduling with Redux Toolkit and RTK Query, improved workflow efficiency by 25%. Hardened security with RBAC/OA

Led full-stack development for Chewy Vet Care Clinic clinical operations & practice management platform supporting expansion across 20+ veterinary clinic locations. Architected a cloud-native microservices platform using React, TypeScript, Node.js, AWS ECS/Fargate/Lambda, API Gateway, Docker, and Terraform, reducing onboarding effort by 35%. Built a GraphQL BFF aggregating clinic, pharmacy, inventory, and customer-portal data using Kafka event streams and Redis caching, cutting cross-system lookup time by 42%. Orchestrated workflow state transitions for appointments, visits, prescriptions, billing, and fulfillment with Kafka domain events, reducing manual status reconciliation by 40%. Integrated RAG-enabled knowledge retrieval for operational documentation, improving cross-team resolution speed by 20%. Modernized appointment scheduling state management using Redux Toolkit and RTK Query (25% efficiency gain), hardened security with OAuth2/JWT/RBAC and API Gateway policies (30% fewer per

Contributed to the Fraud & AML Investigation Operations platform used by fraud analysts and AML compliance teams to investigate suspicious activity across banking/lending/account workflows with 50K+ monthly risk events. Built Node.js/Express services for fraud case lifecycle management and AML alert disposition with RabbitMQ-driven escalation routing on a REST-first architecture. Re-architected investigation console server-state with TanStack Query/React Query to cache and synchronize fraud case data, AML alert queues, and risk score dashboards, reducing analyst triage time from 22 minutes to under 15 minutes for high-priority queues. Introduced Zustand for client-side UI state (filters, layout, preferences), reducing UI bugs by 25%. Synchronized data across PostgreSQL fraud-case records, legacy MySQL KYC system, and Redis risk-profile caching using Prisma ORM for typed access while keeping legacy KYC behind REST adapters, reducing lookup time by 38%. Implemented OAuth2/JWT/RBAC author