B.S. Computer Science and Technology
Coursework: data structures, operating systems, computer networks, computer organization, databases, software engineering, natural language processing. GPA 3.6.
AI-native developer, combining product taste with engineering discipline to turn LLM ideas into traceable, evaluable systems.
“ I judge LLM applications on reproducible evals and end-to-end traces, not on gut feel. ”
AI Native developer focused on traceable Agent, RAG, and workflow systems. My default loop is prototype -> trace -> evaluate -> harden: build quickly, expose tool calls and context, measure behavior with evals, then stabilize with Docker, GitHub Actions, and reviewable workflows. I also treat my LLM Wiki as a Content Operating System for turning project work into reusable knowledge assets.
Pipeline-first AI Resume Ops for bulk applications: parse JDs, generate resume variants, score and rank them, then output application strategy. It treats resume tailoring as an evaluable workflow, not a one-shot prompt wrapper.
AI Code Review Agent with structured output, a ReAct tool-execution loop, and golden-set evaluation. Hard timeout plus budget soft cap cut worst-case single model-call latency 84% (375s -> 60s), while diff-first 80-line windowing cut per-file prefetch context 95.1% (75,466 -> 3,691 chars).
671 contributions in the last 6 months
Coursework: data structures, operating systems, computer networks, computer organization, databases, software engineering, natural language processing. GPA 3.6.
