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Qichen Zhao
Intern Software Engineer specializing in Applied AI and LLM systems
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About
Built and deployed a production RAG-based conversational "Yelp for AI tools" at Search-AI Inc., focused on personalized, explainable AI tool recommendations from thousands of options. Emphasizes production-grade reliability and performance (hybrid retrieval, async two-stage pipelines) and is also building a multi-agent orchestration layer (MAgIc) with typed memory and controlled coordination policies.
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Key Strengths
- Built and deployed a production LLM-powered AI tool recommendation platform (RAG-based conversational search)
- Pragmatic production trade-off decision-making across accuracy, latency, scalability, and cost
- Designed hybrid retrieval (embeddings + keyword + web results) to improve precision and reduce hallucinations
- Architected async-first, two-stage pipeline to reduce unnecessary LLM generation and improve latency
- Strong LLM workflow reliability approach: explicit staged design, unit tests per module, and end-to-end metrics
- Ability to translate technical trade-offs into product outcomes (trust, explainability, perceived speed) with non-technical stakeholders
- Advanced memory/orchestration design for agents (typed memory, short-term vs long-term memory, controlled multi-agent coordination)
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