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Charlotte Yu
Junior Full-Stack AI Engineer specializing in LLM apps and RAG systems
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About
Built and shipped a production LLM-powered “Vet agent” that automates pet symptom intake across multimodal inputs (images/files/text/speech) and provides analysis/home-care guidance, reaching thousands of daily active users within two months. Demonstrates strong agent engineering fundamentals: state-machine orchestration with structured JSON, tool/schema validation, high-availability routing/failover, and rigorous offline/online evaluation loops with trace-driven reliability improvements.
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Key Strengths
- Shipped production LLM agent with ~99% uptime using API routing, timeouts, and failover
- Built multimodal vet-symptom agent (images/files/text/speech) delivering home-care guidance
- Designed multi-step agent as an orchestrated state machine with strict JSON schemas and tool validation
- Implemented predictability mechanisms: constrained actions, schema validation/repair loops, and fallbacks
- Established end-to-end eval/monitoring: offline labeled sets + online tracing/latency/schema-failure metrics
- Improved reliability by adding explicit planning objects, separating execution state from durable memory, and validating tool calls against plans
- Handled messy enterprise data with normalization, boundary validation, fail-closed writes, and idempotent update patterns
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