高雄軟體園區-辦公雲KMS開發工程師

Job Summary 

此職位為偏軟體與資料工程導向的工程師角色，主要負責將實際使用者 (User) 需求轉化為可落地的資料流程、後端服務與視覺化應用。 

工作重心聚焦於資料處理、系統整合、效能與穩定性，並透過與使用單位的密切溝通，持續優化系統與報表，確保資料能被正確且有效地應用於實際業務場景。 

Key Responsibilities 

- 與內部使用者（如工廠、營運或相關單位）溝通需求，釐清實際使用情境，並將需求轉化為具體的系統設計、資料流程或報表規格 

- 使用 SQL、Python 進行資料處理、清洗與轉換，設計與維護穩定的資料管線（ETL / Pipeline） 

- 開發與維護後端服務或 API，支援資料存取、系統整合與應用功能 

- 規劃並開發 BI / Dashboard 視覺化報表（如 Tableau），確保資料正確性、可讀性與實際可用性 

- 與其他工程師、IT 或跨部門團隊合作，進行系統串接、資料整合與流程優化 

- 針對資料量成長或使用情境變化，進行效能調整、架構優化與問題排查 

- 持續關注資料品質與系統穩定性，主動發現並改善影響使用者體驗的問題 

※ 本職位重視將資料與系統「做穩、做對、做給人用」，而非以模型或演算法為主要解決手段。

1. 負責系統平台之 UI/UX 設計 

2. 規劃監控平台之操作流程、資訊架構與使用者體驗 

3. 製作 Wireframe、Mockup 與 Prototype，並與工程團隊協作落地實作 

4. 理解設備資料 (如溫度、流量、告警等)，轉化為直覺化視覺呈現 

5. 協助建構機房或設備之 3D 視覺化呈現 (如空間配置、設備定位) 

6. 製作產品介紹影片、操作教學或訓練素材

1. 測試軟體與網頁平台開發 

2. 伺服器/存儲器測試及功能開發 

3. 軟體部屬測試環境管理規劃

我們正在招募雲平台 Site Reliability Engineer（SRE），負責私有雲平台（OpenStack + Ceph）的穩定性、可用性與可擴展性。 

此角色將深度參與雲平台的設計、維運、自動化與事件管理，透過工程方法提升系統可靠度，而非單純人工作業。 

核心職責（Responsibilities） 

1.負責 OpenStack 雲平台 的穩定性與可用性維運 與 Ceph 分散式儲存 的健康度、效能與容量管理 

2.建立與維護： 

監控（Monitoring） 

告警（Alerting） 

事件回應流程（Incident Response） 

3.進行： 

故障排查（Troubleshooting） 

根因分析（RCA） 

問題事後改善（Postmortem） 

4.透過 Shell / Python 進行自動化，降低人工操作風險 

5.與平台開發、網路、硬體團隊協作，改善整體系統可靠度

我們正在尋找具備雲平台實務經驗的 FAE 工程師，主要負責 OpenStack + Ceph 私有雲平台的客戶技術支援、問題排查與架構說明。 

此職位需能在客戶現場或遠端協助平台部署、維運與障礙處理，並能透過腳本與簡易開發提升支援效率與平台穩定性。 

1. 雲平台 / 儲存基礎 

a. 具備 OpenStack 實務經驗（至少操作或維運過） 

i. 熟悉常見元件：Nova、Neutron、Cinder、Keystone（不要求全精通） 

ii. 了解基本架構與節點角色（Controller / Compute / Storage） 

b. 具備 Ceph 使用或運維經驗 

i. 熟悉 RBD，了解 OSD / MON 基本概念 

ii. 能協助判斷常見容量、效能或異常狀況 

2. 系統與支援能力 

a. 熟悉 Linux 系統操作 

i. 日誌查看、服務管理、效能觀察、問題排查 

b. 具備基本網路概念 

i. TCP/IP、VLAN、Bonding（Overlay Network 有概念即可） 

c. 具備客戶支援或跨部門協作經驗 

i. 能清楚說明問題、同步狀態、協助客戶釐清需求 

3. 腳本與自動化 

a. 能撰寫 Shell Script 

i. 用於部署、巡檢、日常維運或問題快速排查 

b. 具備 Python 基礎能力 

i. 能修改或撰寫簡單工具（如 API 呼叫、資料整理） 

c. 熟悉 Git 基本使用

We are seeking a highly motivated Software Engineer, who support the development, integration, and maintenance of the AI-driven software platform used in GPU server manufacturing and diagnostics.  

This role focuses on log processing, service development, model implementation, data analysis, and integration testing.  

The engineer will work closely with team members to implement features that improve failure attribution, test optimization, and automated diagnostics for AI server platforms.  

**Responsibilities  

1. Software System Development Implement backend service modules for the system. Develop log parsing tools, data transformers, LLM, and rule-based diagnostic logic. Write clean, maintainable code (primarily Python) and participate in code reviews.  

2. Log & Data Handling Build and maintain scripts for collecting, cleaning, and indexing diagnostic logs. Manage log storage and retrieving data for model training.  

3. System Integration & Testing Support integration between the platform modules and Diagnostics software, automation frameworks, and factory systems. Help deploy updated components to test environments or production lines.  

4. Issue Investigation Work with senior engineers to analyze failure logs, detect patterns, and refine rule-based logic. Document root causes and support continuous improvement of diagnostics accuracy.  

5. Documentation & Team Collaboration.

1. 熟悉並了解 NVIDIA DGX/HGX/MGX/IGX 等系統架構。 

2. 熟悉並了解 NVIDIA GPU/CPU/DPU/Networking 等相關元件組成。 

3. IT infrastructure setup 

4. 叢集架構建置與效能分析。 

5. 叢集佈線環境設計。

1. Design, deploy and maintain large scale AI/HPC cluster virtualization environment (Slurm, Kubernetes, Kuberay, Ray, Helm, HTCondor, LSF, DLRover, LamaFacotry, etc) 

2. Orchestra and optimize large scale virtualization enivronment for HA and high performance 

3. Security assessment and implementation on the large scale cluster platform 

4. Scaling and automation system development

1.帶領團隊依專案時程進行軟體分析, 設計及撰寫程式 

2.指導開發人員日常技術工作 

3.設計及管理Web端及後台系統開發流程, 及尋找解決方案並提出具體策略與執行方式 

4.管理整個軟體開發專案生命週期和軟體開發品質管理 

5.依據公司策略及發展，推行各項專案及軟體開發 

6.跨部門溝通協調

Job Description: 

We are seeking a skilled and motivated AI/HPC cluster engineer to design, develop, and maintain a virtualized HPC environment and then deploy to the lab environment and the factory. This role is crucial for enabling our team to develop and test AI/HPC tools in a virtualized environment first, and after verification the complete process, to deploy it on the physical infrastructure.  

[Key Responsibilities] 

Virtual Environment: 

- Build and manage a virtual AI/HPC cluster for development and testing. 

- Configure and optimize networking between virtual nodes to emulate real-world HPC environments. 

- Collaborate with teams to ensure the virtual cluster meets testing and performance requirements. 

- Troubleshoot and resolve networking and system-level issues within the virtual environment. 

- Document system configurations, workflows, and best practices for maintaining the virtual cluster. 

Physical Envrionment: 

- realize and impelemnt the validation test cases into the cluster DUTs.

1. Profiling 

2. Performance analysis & optimization (mainly focus on CPU/GPU/Memory) 

3. Network performance analysis & optimization

設計、開發生成式AI應用產品前端服務 

1. 評估產品客戶功能需求可行性，設計生成式AIGC/LLM應用情境並建置前端服務介面 

2, 數據化用戶操作行為優化產品操作流程及產品功能需求 

3. 跨平台SSO帳號認證及第三方服務API整合

設計、開發和應用生成式人工智慧模型 

1. 評估AIGC/LLM模型及設計產品架構，確定最佳模型實現預期成果 

2, 應用現有數據及工程手段訓練微調AIGC/LLM模型 

3. 將AIGC/LLM模型整合到現有產品中或圍繞模型設計新系統

Job Summary:  

• The engineers should have comprehensive knowledge of AI Large language models, such as GPT-4, and their underlying mechanisms, including an understanding of natural language processing, machine learning, and neural networks. 

Key Responsibilities: 

• Construct, train, and fine-tune large language models to meet specific organizational requirements and goals. Implement strategies for AIGC to create high-quality content. 

• Integrate AI technologies into existing products and services, ensuring they align with business objectives and enhance user experience. 

• Work collaboratively with cross-functional teams and provide technical guidance to non-technical members. Stay updated with AI advancements and educate team members on new developments. 

• Continuously monitor the performance of AI models, making adjustments and improvements based on data analysis and business needs. 

• Maintain effective communication with other teams to ensure collaborative project success. 

• Engage in ongoing learning and research to identify and implement new and appropriate Large Language Model technologies to enhance overall efficiency.

1. 研究雲儲存元件和新功能、新版本開發演進，及系統優化，進行功能導入設計 

2. 雲儲存應用服務整合設計與開發 

3. 協助存儲平台問題釐清解決與設計優化 

4. 開發自動化佈署套件 

5. 協助客戶資料中心運維技術支援

1. Develop Cluster Deployment/Management/Monitor Modules 

2. Big Data Platform Operation (Hadoop, Spark, HBase, ...) 

3. Support Customer to Maintain Big Data Platform

＊了解虛擬化VDI 應用 

＊Restful API呼叫、設計以及程式撰寫。  

＊進行開發前後功能之相容測試與除錯修改、規格技術文件撰寫。  

＊協助研發網站新技術與新工具。  

＊提升自我網站前後端技術，行動應用及網路發展趨勢

• Major tasks focusing on data center devices, including AI accelerators, servers, storage, Ethernet switches, and power shelf. 

• Development of hardware diagnostics software 

• Development of automated testing processes for products 

• Integration or development of software for product stress testing 

• Support for manufacturing test development during new product introduction

Job Summary: 

The Machine Learning engineer will be responsible for computer vision related projects, particularly in advanced image/video processing analyses and utilized for improving product quality, production yield, and factory effectiveness, along with other R&D activities such as algorithm designs, prototype developments and experimental designs.  

Key Responsibilities: 

• Design, implement, and refine advanced Machine Learning/Deep Learning models to support product quality improvement, increasing yield and factory effectiveness. 

• Cultivate strong relationships with production line engineers, IT, and other key stakeholders to ensure alignment of modeling initiatives with company objectives and to identify new hypotheses for model improvements. 

• Scale out modeling capacity by driving infrastructure improvements such as automation of data preparations, model training, implementation, and optimization. 

• Apply deep learning and computer vision techniques, such as image classification, object detection, and support integration of models into tools for use by product engineers and business analysts. 

• Engage with customers to develop and customized data analytics solutions to address special needs. 

• Address ad hoc queries from management and present actionable recommendations in a clear, concise, and convincing manner. 

• Communicate the application and benefits of using various modeling techniques to improve decision-making to customers and stakeholders. 

• Collaborate effectively with team members, whether leading projects or supporting initiatives led by others.  

• Research and evaluate new data technology and state-of-art algorithms, and keep up to date with market trends and competitive insights.

1.數據監控Web程式開發與設計 

2.雲端應用服務功能開發 

3.使用者介面程式開發 

4.軟體模組開發與整合