Intern AI Engineer specializing in LLM agents, RAG, and applied biostatistics
Beijing, ChinaAssistant AI Engineer0 years experienceInternTechnologyArtificial IntelligenceCybersecurity
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About
Siemens AI engineer who shipped production multi-agent LLM systems across cybersecurity and sustainability, including a vulnerability automation agent that cut manual work 70%. Deep in orchestration (LangGraph supervisor-worker state machines), reliability engineering (async fault tolerance, retries, spike handling), and rigorous evaluation (offline benchmarks, LLM-as-a-Judge improving label agreement 28.9%) with measurable production guardrails.
Experience
Assistant AI EngineerSiemens
Research InternEmory University (Nursing School)
Education
Emory Universitymaster, Biostatistics (2026)
Capital University of Economics and Businessbachelor, Data Science and Big Data Technology (2024)
Key Strengths
Built and deployed multi-agent cybersecurity vulnerability automation to production; reduced manual intervention by 70%
Designed reliable LLM systems for peak loads via modularized pipelines and fault-tolerant async execution with retries
Advanced orchestration using LangGraph for cyclical, stateful, dynamically routed multi-agent workflows
Evaluation-driven LLM development using curated offline benchmarks and regression testing
Implemented LLM-as-a-Judge evaluation; improved human-label agreement by 28.9%
Production guardrails focused on temporal stability; kept relative forecasting error bounded within 5%
Strong cross-functional delivery with non-technical sustainability/business stakeholders; reduced request turnaround time by 35%
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PythonJavaScriptTypeScriptSQLRHTMLCSSReactLangChainLangGraphLLM orchestrationLarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)RAG pipelinesMulti-agent systems