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台中 - AI 創新百強🧬：專注醫療場域的 AI 服務機器人與智慧醫療解決方案 🏥在「以科技改善醫療場域」的願景下，您將參與打造新一代智慧醫療 AI 系統，透過生成式 AI 與機器學習技術，讓醫療機器人成為臨床現場可信賴的夥伴。此角色將聚焦於大型語言模型（LLM）與 AI 系統的落地應用，推動智慧醫療解決方案的實際部署與優化。 【工作內容】LLM 應用開發與系統整合 進行大型語言模型（LLM）的調校（fine-tuning）、部署與應用整合結合醫療語境與臨床流程，打造可實際應用於醫療場域的 AI 能力LLM + RAG 推論服務建置規劃並實作 Retrieval-Augmented Generation（RAG）架構 建立穩定、高效的推論服務（Inference Service），支援即時醫療應用場景資料處理與模型開發開發與優化機器學習 / 深度學習模型，提升系統理解與回應能力負責醫療相關資料的整理、清洗與特徵工程 模型效能優化與部署進行模型效能調校（latency / throughput / cost optimization） 支援邊緣部署（Edge AI），讓 AI 能力能在醫療機器人與現場設備中穩定運行

開發與實作 生成式 AI 與影像辨識模型，用於產品瑕疵生成（Stable Diffusion、ControlNet、GAN 及其變體）、資料擴增與品質檢測。應用與微調 影像辨識／表徵學習模型（如 YOLO、CLIP、DINOv3 等新型開源模型）。進行 大型模型（ViT、LLM）Fine-tuning、模型建置、調校與效能優化。建立 資料清理、特徵工程、模型訓練與評估流程，持續提升模型穩定性與準確度。規劃並實作 RAG（檢索增強生成）架構，整合向量資料庫與 LLM。開發與維護 AI 模型服務 API，並與 WPF 前端及其他系統整合。進行 AI 技術與學術文獻研究，將研究成果應用於實務專案。使用 Docker、Git 進行部署與版本控管，配合主管交辦事項與團隊合作。依專案需求配合國外出差（一年約 1--3 次，每次約 3--4 週），參與專案導入、系統部署、模型調校或跨部門協作。其他主管交辦事項，並與團隊密切合作完成專案目標。必備條件3 年以上 AI／機器學習／深度學習 相關工作經驗，熟悉python、C#。熟悉 TensorFlow、PyTorch、Keras 等深度學習框架。熟悉 影像辨識模型與演算法，具 YOLO、CLIP、DINO（含新版本）等實作或應用經驗。具備 生成式模型（GAN 及其變體、Stable Diffusion、ControlNet）實務經驗，並理解模型底層原理。熟悉 Hugging Face 生態系（Transformers、PEFT、Datasets、Tokenizers）。有使用 PEFT 技術經驗（如 LoRA、QLoRA、Prefix Tuning 等），能在有限 GPU 資源下進行有效訓練與調校。了解 Fine-tuning 對模型效能、泛化能力與推論成本的影響，並能依量產需求進行權衡與最佳化。具備 RAG 架構、Prompt Engineering、向量搜尋與任一向量資料庫 經驗。具備 API 開發經驗，熟悉 Docker、Git。具備 數據清理、特徵工程、模型評估與效能優化能力。具良好溝通能力，可獨立作業並進行團隊合作。加分條件曾參與 產品瑕疵檢測、工業影像或製造業 AI 專案。具 影像生成＋影像辨識混合應用 經驗（如生成資料輔助訓練）。具 論文閱讀與模型重現（Reproduction）經驗。具 WPF 前端開發 或 AI 系統整合經驗。曾參與 AI 相關研究、技術分享或開源專案。具 對比學習（Contrastive Learning） 應用經驗（如 Self-Supervised Learning、Representation Learning）。有使用 StableDiffusion WebUI、ComfyUI經驗。

MoMo is the market leader in mobile payments in Vietnam, striving to make all transactions fast, easy, and joyful. You will join our Big Data AI team, where we position AI/Machine Learning as the core component of almost every product feature.Specifically, you will operate as a key technical leader in the Moni team—the squad behind MoMo's flagship AI Assistant. Moni currently serves hundreds of thousands of Monthly Active Users, scaling from a chatbot into a fully autonomous AI Agent. As a Senior / Technical Lead, you will drive the architectural decisions and engineering standards that power the next generation of our Agentic AI.Mô tả công việcTechnical Leadership Architecture:Define the technical vision and architecture for autonomous AI Agents.Make critical decisions on tech stacks, model selection, and system design to ensure scalability and reliability.Architect Build AI Agents: Lead the end-to-end development of complex Agentic workflows (Tool Calling, Planning, Reasoning) that integrate deep into the MoMo ecosystem.Multi-Agent Orchestration: Design and implement orchestration layers where multiple specialized agents collaborate to solve intricate user financial tasks.Advanced RAG Strategy: Engineer robust RAG pipelines (Hybrid Search, GraphRAG, Re-ranking) to handle vast knowledge bases with high precision.System Evaluation Quality Assurance: Establish "Gold Standard" evaluation frameworks for Agentic AI (reasoning capabilities, hallucination rates, safety metrics) and drive the optimization loop.Mentorship Best Practices: Mentor senior/junior engineers, conduct code reviews, and set high standards for code quality, MLOps practices, and GenAI engineering across the team.Production Excellence: Partner with DevOps/MLOps to ensure high availability and low latency for AI services serving massive concurrent traffic.Yêu cầu công việcExperience: 5+ years of professional experience in AI/ML/Software Engineering, with a strong track record in leading technical initiatives.Agentic AI Mastery: Deep hands-on experience in building AI Agents and Multi-Agent systems. Proficient in Agentic Design Patterns like Tool Calling, Planning and Reasoning, and frameworks such as LangChain, LangGraph, or Agents SDK.Advanced RAG Search: Expert knowledge of retrieval strategies, vector databases, and semantic search optimization.LLM Model Strategy: Strong capability in selecting and benchmarking Foundation Models (Open vs. Closed source) and applying fine-tuning/alignment (RLHF, DPO) strategies.System Evaluation: Experience implementing rigorous evaluation pipelines for Agentic AI (using Ragas, Langfuse, or custom metrics).Engineering Excellence: Proficient in Python, PyTorch, and modern Data/AI stacks. Experience in designing high-load distributed systems is a plus.Leadership Mindset: Ability to navigate ambiguity, drive technical consensus, and balance engineering perfection with product delivery speed.

About BTSE: BTSE Group is a global leader in fintech and blockchain technology, anchored by threecore business pillars: Exchange, Payments, and Infrastructure Development. Servingover 100 corporate clients worldwide, we provide white-label exchange and paymentsolutions. Our offerings encompass everything from exchange infrastructure hostingand development to custody, wallets, payments, blockchain integration, trading, andmore.We are looking for talented professionals in marketing, operations, customer support,and other departments. The roles offered may be on-site, remote, or hybrid, incollaboration with our local partner. About the opportunity: You own the AI core: model serving, the retrieval-augmented generation (RAG) pipeline, prompt engineering, and the feedback-to-training pipeline. In Phase 1, you make the base model perform as well as possible through context engineering — system prompts, few-shot exemplars, and retrieval optimisation — without modifying model weights. You also design the custom model training workflow so that enterprise clients can train their own fine-tuned models in Phase 2. This is the highest-leverage individual contributor role on the founding team.ResponsibilitiesDeploy and optimise a large language model for production inference: quantisation, continuous batching, low-latency serving. Build the RAG pipeline: document chunking, embedding generation, vector storage, cross-encoder reranking, and context assembly optimised for a 128K-token context window. Build the context layer: per-tenant system prompts, dynamically retrieved few-shot exemplars, task routing (classifying incoming requests to the right prompt configuration). Build defensive output parsing: structured JSON output from an unmodified base model with graceful fallbacks. Design and implement the feedback collection pipeline: capturing user corrections and ratings, automatically generating training data candidates for future fine-tuning. Design the custom model training workflow: tenant-scoped LoRA training on client-specific data, model evaluation, A/B testing, and isolated deployment. Monitor and improve inference quality: parsing failure rates, citation accuracy, hallucination rates, latency — all tracked per tenant. Iterate on prompts daily with the domain expert during the pilot phase.Requirements 5+ years ML engineering; 2+ years working with large language models in production. Hands-on experience with LLM serving frameworks (vLLM, TGI, or equivalent). Deep experience building RAG pipelines: chunking strategies, embedding models, vector databases, reranking. Strong prompt engineering skills for production applications — you know how to make a base model produce consistent, structured, high-quality output. Python: PyTorch, Transformers, FastAPI. Familiar with LoRA/QLoRA fine-tuning workflows. Nice to have Experience building multi-tenant ML serving infrastructure. Experience with financial or crypto AI applications. Experience with cross-encoder reranking models (DeBERTa or similar). Understanding of data isolation requirements for ML training pipelines. #LI-MC1

About BTSE: 彼特思方舟 is a specialized service provider dedicated to delivering a full spectrum of front-office and back-office support solutions, each of which are tailored to the unique needs of global financial technology firms. 彼特思方舟 is engaged by BTSE Group to offer several key positions, enabling the delivery of cutting-edge technology and tailored solutions that meet the evolving demands of the fintech industry in a competitive global market. BTSE Group is a leading global fintech and blockchain company that is committed to building innovative technology and infrastructure. BTSE empowers businesses and corporate clients with the advanced tools they need to excel in a rapidly evolving and competitive market. BTSE has pioneered numerous trading technologies that have been widely adopted across the industry, setting new benchmarks for innovation, performance, and security in fintech. BTSE’s diverse business lines serve both retail (B2C) customers and institutional (B2B) clients, enabling them to launch, operate, and scale fintech businesses. BTSE is seeking ambitious, motivated professionals to join our B2C and B2B teams. About the opportunity: You own the AI core: model serving, the retrieval-augmented generation (RAG) pipeline, prompt engineering, and the feedback-to-training pipeline. In Phase 1, you make the base model perform as well as possible through context engineering — system prompts, few-shot exemplars, and retrieval optimisation — without modifying model weights. You also design the custom model training workflow so that enterprise clients can train their own fine-tuned models in Phase 2. This is the highest-leverage individual contributor role on the founding team.ResponsibilitiesDeploy and optimise a large language model for production inference: quantisation, continuous batching, low-latency serving. Build the RAG pipeline: document chunking, embedding generation, vector storage, cross-encoder reranking, and context assembly optimised for a 128K-token context window. Build the context layer: per-tenant system prompts, dynamically retrieved few-shot exemplars, task routing (classifying incoming requests to the right prompt configuration). Build defensive output parsing: structured JSON output from an unmodified base model with graceful fallbacks. Design and implement the feedback collection pipeline: capturing user corrections and ratings, automatically generating training data candidates for future fine-tuning. Design the custom model training workflow: tenant-scoped LoRA training on client-specific data, model evaluation, A/B testing, and isolated deployment. Monitor and improve inference quality: parsing failure rates, citation accuracy, hallucination rates, latency — all tracked per tenant. Iterate on prompts daily with the domain expert during the pilot phase.Requirements 5+ years ML engineering; 2+ years working with large language models in production. Hands-on experience with LLM serving frameworks (vLLM, TGI, or equivalent). Deep experience building RAG pipelines: chunking strategies, embedding models, vector databases, reranking. Strong prompt engineering skills for production applications — you know how to make a base model produce consistent, structured, high-quality output. Python: PyTorch, Transformers, FastAPI. Familiar with LoRA/QLoRA fine-tuning workflows. Nice to have Experience building multi-tenant ML serving infrastructure. Experience with financial or crypto AI applications. Experience with cross-encoder reranking models (DeBERTa or similar). Understanding of data isolation requirements for ML training pipelines. #LI-MC1

1. 設計與開發 AI Agent 系統，用於 BIOS / Firmware / DevOps 流程自動化 2. 建構 LLM-based 工作流程（如：Code Review、Log 分析、BIOS 設定生成） 3. 整合 AI 模型（OpenAI / 本地LLM）與公司內部系統（GitLab、Redmine、CI/CD） 4. 建立知識庫（RAG），整合 BIOS 文件、Spec、Debug Log 5. 與 BIOS / Software / IT 團隊合作導入 AI 解決方案

MoMo is the market leader in mobile payments in Vietnam. We strive to make all transactions fast, easy and joyful. We are looking for an experienced AI Engineer to join our growing Big Data AI team. At MoMo, we make AI/Machine Learning the core component to almost every part of the product - product recommendation, personalization, conversational AI, eKYC, risk scoring, fraud detection, promotion targeting and financial services. As a Senior AI Engineer, you will play a leading role in developing our Customer Service Chatbot system, delivering intelligent and seamless support experiences to millions of users.Mô tả công việcDesign, build, and optimize LLM-powered chatbot systems for customer service at scale;Develop and implement RAG (Retrieval-Augmented Generation) pipelines, prompt engineering strategies, and agentic workflows for domain-specific performance;Architect conversation flows, intent handling, and dialogue management systems that handle complex multi-turn interactions;Build evaluation frameworks and monitoring systems to measure chatbot quality, detect hallucinations, and ensure response accuracy;Collaborate cross-functionally with product, customer service operations, and engineering teams to continuously improve chatbot capabilities;Write production-grade code and maintain robust, scalable AI systems serving millions of users;Stay current with LLM advancements and evaluate new models, techniques, and tools for potential adoption.Yêu cầu công việc5+ years of experience as an AI Engineer, with at least 2 years focused on LLM applications or conversational AI systems;Deep understanding of LLM architectures, capabilities, and limitations (GPT, Claude, LLaMA, etc.);Experience building chatbot or virtual assistant systems, particularly for customer service use cases;Fintech domain experience is a strong plus;Proficiency in RAG systems, vector databases, embedding models, and prompt engineering techniques;Strong software engineering skills in Python. Experience with LLM tooling (LangChain, LlamaIndex, etc.);Experience with system design for high-availability, low-latency services;Familiarity with evaluation methods for generative AI systems and conversation quality metrics.

【職責範圍 Responsibilities】 打造 Agentic 自動化工作流：基於我們核心的多模態 RAG 引擎，設計並部署多步驟的 AI 工作流 (Agentic workflows) 與決策路由邏輯，並與我們的核心 API 無縫互動。確保 AI 輸出的穩定與合規 (Guardrailing)：設計嚴格的輸入/輸出防護機制，防範 Prompt Injection、過濾敏感個資 (PII)，並確保 LLM 產出 100% 符合預期的資料格式 (如嚴格的 JSON Schema)，以便安全地傳遞給下游系統。建構穩健的企業系統整合與 ETL：開發高容錯、安全的 API 連結，串接各大企業系統 (如 SAP, Oracle, NetSuite, QuickBooks, Snowflake)，並維護可靠的 ETL 資料管道，將傳統企業數據同步至現代多模態或向量資料庫 (如 SurrealDB, Qdrant, ChromaDB)。設計 Human-in-the-Loop (HITL)：針對關鍵的商業操作（如下單、寫入 ERP），設計並整合「人機協作審核」介面，確保系統的最終安全性與準確率。效能與成本優化：優化 LLM Prompt 表現、精準控管 API 成本，並針對脆弱的第三方 API 設計積極的錯誤處理 (Error handling) 與重試機制 (Retry logic)。

Minimum qualifications: Bachelor’s degree or equivalent practical experience. 5 years of experience with software development in one or more programming languages. 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture. Experience in developing and deploying Generative AI agents. Experience with storage systems design, implementation, and management. Experience in natural language processing (NLP), image generation, or other generative AI techniques. Preferred qualifications: Master's degree or PhD in Computer Science or related technical field. 5 years of experience with data structures and algorithms. 1 year of experience in a technical leadership role. Experience in text/code embeddings at scale, and with C++ programming language. Experience with infrastructure and building scalable distributed systems. Knowledge of GenAI, RAG, Agents. About the jobGoogle's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. We work to create a seamless and intuitive exploration agent that accelerates development workflows and fosters knowledge sharing across Google.In this role, you will be at the forefront of next generation code exploration, shaping the future of developer agents, improving the productivity and efficiency of developers and agents across Google, accelerating innovation and product development. You will collaborate with engineers, researchers, and designers across various teams and disciplines, contributing to a critical platform with opportunities for learning, growth, and career development. You will work on a platform that indexes and searches billions of lines of code, serving thousands of Google engineers daily.The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.Responsibilities Partner with agent teams (e.g., Gemini Coder, Duckie, Basecamp) to create sub-agents, tools, RAG solutions and workflows enabling them to do better code exploration. Integrate LLMs, RAG skills to augment code search with features like natural language code search, chat agents. Design, develop, and maintain core features of the Google code search platform, used by thousands of engineers daily. Improve the efficiency and scalability of code search infrastructure, ensuring fast response times and high availability. Analyze user data and conduct A/B testing to measure the effectiveness of new features and identify areas for improvement.  Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

- Assist in the design and development of NLP pipelines for tasks like entity recognition, summarization, sentiment analysis, and question answering.- Preprocess and clean text data for training and evaluation of LLMs.- Experiment with state-of-the-art LLM frameworks (e.g., Hugging Face Transformers, OpenAI API, LangChain) to solve real-world text processing problems.- Support fine-tuning of pre-trained models on domain-specific datasets using techniques like LoRA, PEFT, or full model training.- Develop and maintain prompt engineering strategies for improving model performance.- Research and implement techniques for RAG (Retrieval-Augmented Generation) and context optimization.- Document experiments and present findings to the team.