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Description SWAG is building AI-native product capabilities powered by LLM-based agents and multi-modal intelligence. These systems are not experimental prototypes — they operate in production, interact with real users, and directly impact product experiences and business outcomes. We are looking for a AI Engineer to design, build, and operate AI agent systems that integrate large language models, vision models, internal data platforms, and external tools. You will work closely with Data, Product, and Engineering teams to deliver scalable, reliable, and observable AI systems. Responsibilities 1. Design and implement LLM-based AI agents that support multi-step reasoning, tool / function calling, and contextual decision-making. 2. Build and maintain agent workflows that interact with: Internal data platforms and services External APIs and third-party tools Product events and user signals 3.Integrate computer vision and multi-modal models (vision + language) into agent workflows and product features. 4. Apply vision models for use cases such as content understanding, classification, moderation, or personalization. 5. Own the end-to-end lifecycle of AI systems, including: Prompt and agent versioning Model selection, upgrades, and deprecation Online inference services and deployment 6. Implement LLMOps practices, including: Automated evaluation of LLM outputs Monitoring quality, latency, usage, and cost Identifying and mitigating real-world failure cases 7. Collaborate with Data Engineers to integrate AI systems into existing infrastructure, CI/CD pipelines, and observability platforms. 8. Work closely with Data Scientists, Product Managers, and Software Engineers to translate business requirements into production-ready AI solutions.

隨著企業數位轉型的步伐加快，AI automation 應用技術已成為提升效率和競爭力的重要工具。Aiworks 致力幫助企業組織痛點需求做出發，從策略規劃到落地執行，量身打造完整的 AI 自動化導入方案：涵蓋培訓與陪跑顧問服務，未來依可行性需求也將提供專業團隊駐點協作與開發支援，確保企業快速掌握技術、加速成效落地。我們正在尋找 Enablement Program Manager (AI Solutions)，你將成為技術與應用之間的橋樑。負責深入訪談企業客戶以釐清核心痛點與學習需求，並以此為基礎設計 AI 自動化相關課程，並確保課程從企劃到執行的全生命週期皆能順利運作與持續優化。如果你對 AI 教育、自動化應用及協助企業數位轉型充滿熱情，歡迎加入我們的團隊！ 【工作內容】 需求診斷與協作協同顧問導師與 B2B 客戶/學員課前訪談，釐清企業客戶的痛點與學員樣貌，並協助將需求轉化為合適的 AI 自動化培訓建議。內容轉化與品質把關持續追蹤 AI 與自動化工具趨勢，負責將複雜技術概念轉化為易懂的學習語言，並與設計團隊/講師協作，確保教材視覺與教學品質符合落地應用。專案交付與客戶管理負責課程專案的全生命週期管理，確保從教學設計到實地交付的流程順暢；並作為客戶的核心溝通窗口，追蹤學員的學習曲線與技能轉化成效。解方演進與數據優化透過量化數據分析（學員滿意度、完課率）與質化意見回饋，持續迭代課程產品，確保教學體驗能緊隨 AI 技術更新並滿足市場需求。

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade. The AI4BI team serves as the central intelligence hub within the Corporate Planning Organization (CPO). Our mission is to empower business users with AI-driven solutions that enhance customer satisfaction and streamline operational efficiency. We work closely with stakeholders who blend deep domain expertise in areas like demand and capacity planning with strong analytical capabilities.As an AI Engineer in the Product Innovation department, you will collaborate closely with product managers, designers, and internal business users to prototype and integrate new features into products. You are self-motivated and naturally curious, driven to innovate and deliver impactful results. Responsibilities: 1. Drive Innovation and Impact: Pioneer novel AI/ML solutions from initial concept to functional prototype. Shape the future of our internal product with your discoveries.2. Collaborate with Business Users: Work closely with internal stakeholders to understand needs and translate them into actionable AI-driven solutions that create measurable value.3. Apply Smart AI Techniques: Choose and implement the appropriate methods—classic machine learning, deep learning, or generative AI—to effectively solve diverse problems.4. Partner Across Teams and Geographies: Collaborate with cross-functional, global teams including AI researchers, engineers, and domain experts to integrate AI into production systems.5. Share Knowledge and Stay Current: Communicate insights through data storytelling, mentor colleagues, and proactively adopt emerging AI/ML trends to continuously improve solutions.

We are looking for a skilled AI Automation Engineer to support us on a freelance, per-project basis. In this role, you will help us design, build, test, and validate automation solutions using Claude Code, Make, and n8n. You will work from a clear process definition and help translate it into reliable, maintainable automation workflows. This is not a client-facing role. You will collaborate internally with our team, while we handle the client relationship, process discovery, and client communication. Your focus will be on execution, technical problem-solving, testing, and making sure the automation works as expected. Responsibilities Automation Design Build Build automated workflows based on documented client processes and requirements using Claude Code, Make, and/or n8n. Workflow Integration Connect tools, apps, APIs, webhooks, databases, and data sources using Make or n8n, depending on the project requirements. Claude Code Development Use Claude Code to support automation development, scripting, workflow logic, debugging, technical implementation, and custom components where needed. MCP AI Tooling Integration Work with MCPs, AI models, connected tools, and structured prompts to extend automation capabilities and create more intelligent workflows. Process Validation Help validate that the automation logic is aligned with the client’s approved process, business rules, expected outputs, exceptions, and edge cases. Testing Error Handling Test workflows thoroughly, identify bugs, handle errors, manage fallback scenarios, and ensure automations are stable before delivery. Optimization Maintainability Improve existing workflows for reliability, maintainability, performance, cost efficiency, and ease of future updates. Documentation Create clear technical documentation explaining workflow logic, integrations, MCPs, APIs, dependencies, credentials required, error handling, and maintenance notes. What We’re Looking For We are looking for someone who: Has proven experience building automations with Make and/or n8nIs comfortable using Claude Code for technical implementation, debugging, scripting, or workflow developmentUnderstands how to translate a business process into a reliable automation workflowHas experience working with APIs, webhooks, JSON, HTTP requests, and data transformationsUnderstands or is willing to work with MCPs and AI-assisted workflow developmentCan structure workflows with proper error handling, logging, and fallback logicCan work independently from a clear briefPays strong attention to detail and tests their work carefullyCan document their work clearlyIs comfortable working project by project as a freelancer Experience with prompt engineering, AI agents, structured outputs, data parsing, databases, authentication, and workflow monitoring is a strong plus. Work Arrangement Location: 100% remoteType: Freelance / ContractEngagement: Per projectClient-facing: NoRate: Please provide your rate per project or per automation when applying. How to Apply Please send: A short introductionYour experience with Claude Code, Make, and/or n8nExamples of previous automation projectsYour experience with APIs, webhooks, MCPs, and AI workflow developmentThe tools you are most comfortable withYour rate per project or per automationYour availability We are looking for a reliable freelance partner who can help us turn validated business processes into robust, practical automation solutions using Claude Code, Make, n8n, MCPs, APIs, and AI-assisted development practices.

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade.Responsibilities: AI algorithm/application research and development on image recognition, time series forecasting, and abnormal detectionAI system design and development, including system infrastructure (docker/K8S) and web application.AI System analysis, design, development and integration for cross organizations/systems projects.Innovate, develop and introduce Intelligent Manufacturing Solutions.Hiring Organization: IMC

【投遞須知】請意欲參與此實習的夥伴，請檢附相關開發資訊，如GIT HUB等，以利技術性審核，感謝!這是一份要動起來的實習旅程，會面對硬實力與軟實力的複合型挑戰!"近期主題偏向NLP、CV之產品化開發。*持續且頻繁使用超過1年的Python經驗，具良好coding style*複合型的應用開發，包括AI技術(NLP/CV/BIG DATA )為主軸、後端產品化(DATABASE/API)為輔【工作內容】1. 負責AI軟體與系統化之資料蒐集、設計、程式撰寫、測試、優化與佈署等2. 依照專案需求建立AI模組並配合軟硬體整合開發3. 針對現有產品、模型進行優化、調參 4. AI模型系統化之環境與後端API、資料庫串接製作5. 文件化與基礎QAQC【加分項目】● 資訊工程科系尤佳，具備自主開發的能力● 具備物件偵測(Object Detection)相關開發經驗● 產品化面:熟悉My SQL資料庫運用/NoSQL資料庫運用/Linux作業系統開發/Docker● AI模型面:熟悉深度學習架構、演算法技術與模型加速技術、NLP● 熱愛軟體開發，積極有耐心【實習時程】● 以長期穩定實體實習為主，時程至少6個月含以上，一週至少能配合3天。

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers’ products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world’s largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade.Advanced Quality System Development (AQSD) is part of TSMC's Intelligent Manufacturing Center (IMC), responsible for three main directions: Use statistical methods and conditional monitoring to inspect fab control charts, providing early warning of abnormal product yields; offer a fab defense system settings comparison platform to check that machines meet the internal control anomaly conditions set by the engineering department.Develop online fab analysis platforms through Big Data, Machine Learning, and Deep Learning techniques for online yield/defect analysis to ensure the quality of wafers produced in the fab.Develop systems and provide logical operations to control and implement escape mechanisms for material batch changes, measurement stages, and inspection stages, ensuring quality and optimizing product sampling rules. Responsibilities: Apply machine learning/optimization algorithm to build up models for optimizing semiconductor production.Develop analysis and optimization methods to enhance product quality, increase tool productivity and improve people productivity.Design, develop and test prediction models with real applications in manufacturing.Build a flexible framework to speed up the development process of AI models, with a focus on Large Language Models (LLMs) serving.Turn exciting AI prototypes/ideas into products, leveraging LLMs and other advanced AI technologies.Develop next-generation AI backend systems related to large-scale real-time data access, collection, analytics and monitoring.Establish and maintain MLops processes and tools, including model deployment, monitoring, and automation.Continuously improve the quality of AI production systems, particularly those utilizing LLMs.

公司主要從事SES（系統工程服務）事業，目前手上擁有許多與AI領域相關的案件。 預計未來AI相關需求將持續成長，目前正處於事業擴展的成長階段。 即使目前尚無AI相關經驗，只要具備工程師經驗，也有許多案件可以參與。 我們積極招募有志於投入AI領域、希望累積經驗、提升技能與市場價值的人才！ 【案件範例】 ・生成式AI基礎平台開發支援 ・對話型AI的研究／開發／導入 ・銷售管理系統的API伺服器建置 ・雲端與銷售管理系統的整合開發 ・打造在元宇宙空間中進行互動的應用程式開發 【工作內容範例】 ・大型語言模型（LLM）的標註工具開發 ・網站爬蟲系統開發 ・以後端為主的全端開發 ・探索將生成式AI應用於既有服務的方式 ・利用現有的基礎模型（LLM）進行微調與相關研究／開發

建構與優化機器學習模型：運用多維度運動數據（如賽事數據、球員表現），開發球員薪資計算、表現預測等核心模型，並持續進行優化。支援產品與行銷決策：深入分析用戶行為與市場趨勢，提供數據洞察，協助產品團隊優化功能、支援行銷團隊制定精準策略。整合生成式 AI 與 Agent 應用：導入並開發生成式 AI 功能與對話式代理應用，例如自動化產出運動新聞、球員動態、賽事摘要，以及可支援用戶查詢、內容推薦與任務自動化的 agent，豐富產品內容與互動體驗。提升內部數據工作流程效率：運用 AI 技術優化資料處理、分析與建模的流程，提升團隊整體開發效率。

在 Canner，我們正在打造突破性的 AI 與數據基礎架構解決方案，讓數據不再只是靜態資產，而是企業決策的核心動力。加入我們的研發團隊，你將參與開發和推動 Wren AI (https://github.com/Canner/WrenAI)。 Wren AI 是 Canner 的旗艦開源專案，讓大型語言模型 (LLM) 能夠直接查詢企業數據，為 AI Agent 提供精確的語意查詢能力。我們的開源庫已連續數週蟬聯 GitHub Top Trending，目前累積將近 10k+ 顆星，成為全球主流的 GenBI 解決方案。這代表了全球開發者對這項技術的高度認可與需求。 如果你熱衷於數據基礎架構、查詢引擎及大規模 AI 應用的開發，這將是參與業界最前沿技術發展的絕佳機會。 職責 (Responsibilities) 設計與開發 Wren AI 與 Wren AI Service參與核心語意查詢引擎開發，透過 LLM 打造 生成式 AI 應用 (Gen AI app)，提升 AI 在數據查詢與分析上的精確度與實用性與數據團隊和前端團隊合作，將 Wren AI 整合到實際應用場景中，驅動更強大的 AI 解決方案。開源專案維護與推廣積極參與並推廣 Wren AI 開源專案，處理社群反饋與技術問題，與全球開發者社群保持互動。協助開源社群解決使用上的問題，並將關鍵反饋回饋給內部研發團隊，推動產品迭代與優化。跨部門合作與 Data Team 與 Web Team 緊密合作，參與從數據查詢引擎到前端應用的全流程開發與部署，確保 AI 服務的穩定性與可擴展性。技術探索與創新保持對 LLM 技術的高度敏感度，積極探索最新的 AI 技術趨勢 (如 RAG 模型或大型語言模型微調)，並思考如何將其應用於實際業務場景，設計更智能、更好用的 AI 功能。