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Jiacheng Yin
Intern Software Engineer specializing in data engineering and AI agent systems
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About
AI engineer at Anote.ai who built and shipped a production multi-agent LangGraph/LangChain/Ray RAG platform for enterprise search and workflow automation, supporting 3 commercial products and 100+ developers. Drove measurable gains (30% accuracy improvement, lower latency) and improved reliability with Redis-based state checkpointing, message-queue synchronization, and Milvus retrieval optimizations, while partnering with PMs/clients to add transparency features like confidence scores and real-time logs.
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Key Strengths
- Built and deployed production multi-agent AI system for enterprise RAG search and workflow automation
- Designed LangGraph state-machine orchestration with checkpointing via shared Redis to enable recovery and retries
- Improved RAG accuracy by 30% while reducing latency via LangChain + Ray pipeline optimizations
- Scaled system to support 100+ developers and 3 commercial products
- Improved reliability under concurrency using message queues to synchronize updates and prevent race conditions
- Performance-focused retrieval optimization (semantic chunking, batch embedding, Milvus index preloading, vector caching)
- Strong evaluation/observability practice (unit/integration tests, synthetic benchmarks, Grafana dashboards)
- Effective collaboration with non-technical stakeholders; added explainability features (real-time logs, confidence scores)
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