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Ryan Crabbe
Junior Full-Stack/Mobile Engineer specializing in React Native and NestJS
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Built an AI-powered restaurant menu rewriting app that generates diet-constrained menus from photos, with a backend designed around bounded contexts and a lightweight CQRS approach. Demonstrates strong multi-tenant PostgreSQL design (RLS, tenant-scoped queries) and performance tuning (partitioning, keyset pagination, composite/partial indexes), plus AI workflow orchestration using Redis/BullMQ and Vercel AI SDK with structured outputs and evals; reduced p95 latency ~35–50% via racing LLM requests and caching.
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Key Strengths
- Backend architecture using bounded contexts and lightweight CQRS for rapid iteration
- PostgreSQL performance optimization (keyset pagination, composite/partial indexes, partitioning with pruning)
- Multi-tenant data isolation with PostgreSQL RLS and tenant-scoped query filtering
- AI workflow orchestration with Redis/BullMQ and structured LLM outputs validated via Zod
- Latency optimization via concurrent/racing LLM requests (p95 reduced ~35–50%)
- Practical caching to eliminate duplicate AI work (cache keyed by menu_id and constraint_hash)
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