AI Agent Engineer | RAG Engineer | Full-stack Developer

Building evidence-grounded LLM applications for professional document intelligence, review automation, and reliable AI workflows.

Contact
Email: 2856006827@qq.com
GitHub: @konoeph
Open to: AI Agent systems | RAG engineering | document intelligence | review automation

I come from a New Energy Science and Engineering background and now focus on practical AI engineering for complex professional content.

My work sits at the intersection of:

• Agent workflow design and ReAct-style task decomposition
• RAG pipelines with hybrid retrieval, reranking, and evidence tracing
• Claim-level verification and evidence-constrained generation
• Document parsing, OCR coordination, table extraction, and cross-section consistency checks
• Full-stack delivery with testable backend services and maintainable product structure

I care about making LLM systems not only fluent, but also traceable, auditable, and useful in real workflows.

This is the shape of work I enjoy most: turning long, messy, professional documents into structured review pipelines with retrieval, evidence binding, and grounded outputs.

I design Agent workflows that combine tool calling, retrieval, intermediate verification, and structured outputs to make model behavior more dependable in production settings.

I build retrieval and review pipelines for engineering and business documents where answers need evidence, not just fluency. That includes chunking, embedding search, hybrid retrieval, reranking, attribution, and review-oriented issue generation.

I turn model capabilities into usable systems with backend services, frontend integration, persistence, testing, and deployment. The goal is not demo-only AI, but software that teams can actually operate.

A lightweight framework for claim-level guardrails and evidence checks in LLM applications.

Core ideas:

• Python SDK and Pydantic schemas
• YAML policy runtime
• validator pipeline for claim checks
• FastAPI service surface
• tests, docs, demos, and CI-friendly structure

Repository: konoeph/AgentClaimGuard

An AI review workflow for feasibility reports and engineering materials, designed to surface issues such as inconsistent numbers, missing evidence, weak argumentation, unsupported conclusions, and cross-section contradictions.

Typical pipeline:

• OCR and document parsing
• chunking and indexing
• hybrid retrieval and reranking
• evidence tracing
• structured issue generation
• LLM-based review orchestration

A document automation workflow for feasibility study reports that handles data injection, field mapping, table replacement, and report assembly across Word and Excel sources.

Focus areas:

• section-anchor matching
• table header matching
• partial replacement
• full table replacement
• consistency improvement for long technical reports

Languages & Backend
Python | FastAPI | Pydantic | SQLite | REST APIs

AI Engineering
LLM Agents | RAG | Embeddings | Rerankers | Tool Calling | Structured Output | Prompt Engineering | Evaluation

Document Intelligence
PDF / Word parsing | OCR workflows | Table extraction | Evidence tracing | Cross-section consistency checking

Workflow & Delivery
Git | GitHub Actions | pytest | OpenAI-compatible APIs | ChatGPT | Codex | Cursor

Current Deployment Interests
Local LLM deployment | vLLM | long-context optimization | private knowledge base systems

Right now I am focused on:

• building reliable Agent systems for professional document review
• improving evidence-grounded RAG with reranking and citation binding
• designing claim-level guardrails for LLM outputs
• connecting local model deployment with practical engineering workflows
• turning AI prototypes into maintainable full-stack products

I am especially interested in collaborating on:

• AI Agent engineering
• RAG systems
• document intelligence
• professional review automation
• open-source tooling for reliable LLM applications

If you are building practical AI systems or evidence-constrained LLM products, feel free to reach out at 2856006827@qq.com.