AI 工程師

Key Responsibilities (工作內容) -

1. Cross-functional Collaboration & Process Understanding / 跨部門協作與流程理解
Work with business and technical teams to understand workflows and identify opportunities for AI-driven improvements.
與業務及技術團隊合作，理解流程與系統運作，辨識可導入 AI 的優化機會。

2. AI Solution Development & Deployment / AI 解決方案開發與部署
Design, develop, and deploy AI-powered applications or agents to support automation and operational efficiency.
設計、開發並部署 AI 應用或 Agent，以提升自動化程度與營運效率。

3. AI Agent Design & Implementation / AI Agent 設計與實作
Build AI agents with proactive monitoring and task execution capabilities (e.g., routine checks, data tracking).
開發具備主動監控與任務執行能力的 AI Agent（如例行檢查、數據追蹤）。

4. Prompt Engineering & Behavior Design / Prompt 設計與行為定義
Develop and refine system prompts to ensure consistent AI behavior, role definition, and output quality.
撰寫與優化系統提示詞（System Prompts），建立 AI 行為邊界與角色定位，確保輸出穩定一致。

5. Workflow Automation / 流程自動化
Translate business requirements into scalable, automated AI workflows to improve efficiency and reduce manual effort.
將業務需求轉化為可擴展的 AI 自動化流程，降低人工操作並提升效率。

6. Model & Knowledge Optimization / 模型與知識優化
Improve performance through prompt tuning, retrieval (RAG) optimization, or lightweight fine-tuning for domain-specific use cases (e.g., RFID, supply chain).
透過 Prompt 調整、RAG 架構優化或基礎微調，提升 AI 在特定領域（如 RFID、供應鏈）的表現。

7. System Integration & Implementation Support / 系統整合與導入支援
Collaborate with engineering and IT teams to integrate AI solutions into existing systems and support implementation.
與工程及 IT 團隊合作，將 AI 解決方案整合至既有系統，並支援導入落地。

8. Testing, Monitoring & Continuous Improvement / 測試、監控與持續優化
Test, monitor, and iterate on AI solutions based on performance metrics, user feedback, and operational needs.
執行測試、監控系統成效，並依據數據與使用者回饋持續優化 AI 應用。