Resume / AI Application Developer
AI Application Developer
Target role: AI Application Developer. My edge is turning AI Agent capabilities into usable applications across backend implementation, frontend interaction, Agent architecture, context governance, and real delivery.
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Advantages
For AI application developer roles, the key evidence is engineering implementation, Agent architecture understanding, complex project execution, and real delivery.
Independently drove Plato from proof of concept to a usable Agent application, covering backend capabilities, frontend GUI, interaction design, desktop packaging, and release. The application currently supports macOS ARM delivery through a DMG package.
Designed Plato's underlying architecture around a ReAct abstraction with session, plan, and task-level context governance. It supports Skill integration, Agent clarification, user interruption and recovery, and event records for key changes.
Plato has grown beyond 100k lines of code and 100k lines of documentation. I continue to maintain project assets, iterate features, and keep delivery moving under meaningful complexity.
Built a repeatable workflow across requirement framing, planning, design, implementation, testing, acceptance, packaging, and release, while consistently reviewing failure paths, complexity risks, and delivery gaps.
Feature and architecture decisions start from user tasks: how users understand AI, describe requirements, inspect results, and whether system complexity turns into real user value.
Quickly strengthened product, UI, frontend, context governance, Skill, and data-flow capabilities in roughly one and a half months, then applied them directly to product construction.
Project evidence
Plato is the core long-running project that demonstrates the full loop from requirements and architecture to implementation and delivery.
Primary project
An AI-assisted product workbench for turning ambiguous product work into durable plans, documents, and verifiable execution loops.
AI-assisted build evidence
This data is not meant to prove that many tokens were consumed. It is supporting evidence of practice density: sustained AI-assisted product design, architecture analysis, code implementation, article editing, and publishing work. What matters is that the intensity turned into a working product, public repository, project documents, and a writing series.
Skills
