我們正在尋找一位充滿熱情且技術熟練的AI工程師加入我們的團隊，負責設計、開發和實施人工智慧解決方案。你將與跨職能團隊合作，利用機器學習、深度學習和資料分析技術來解決複雜問題並推動產品創新。

主要職責

• 設計並開發AI模型，包括機器學習和深度學習演算法，以滿足業務需求。

• 清理、預處理和分析大規模資料集，確保模型輸入資料的品質。

• 將AI模型整合到現有系統或應用程式中，並優化其效能與可擴展性。

• 與資料科學家、軟體工程師和產品經理合作，定義專案目標並交付成果。

• 持續監控和改進已部署的AI系統，確保其準確性和可靠性。

• 研究最新的AI技術和趨勢，提出應用於公司產品的創新建議。

• 撰寫清晰的技術文件，記錄模型開發過程和部署細節。

• 開發和實施大型語言模型（LLMs）、檢索增強生成（RAG）系統、AI代理和基於圖的AI解決方案，以增強智慧系統。

• 優化LLMs以適應特定用例，包括微調、提示工程和生產環境中的部署。

• 設計和構建RAG管道，整合外部知識來源，提升模型準確性和上下文相關性。

• 創建具備任務規劃、決策制定和多步推理能力的自主AI代理。

• 利用基於圖的AI技術，如知識圖譜和圖神經網路，建模複雜關係並增強決策能力。

技能與資格要求

• 學歷：電腦科學、資料科學、數學、工程或相關領域的學士學位（碩士或博士學位尤佳）。

• 經驗：至少2-3年在AI、機器學習或相關領域的實務經驗。

• 程式語言：精通Python，熟悉相關套件（如TensorFlow、PyTorch、Scikit-learn、Pandas、LangChain、LlamaIndex）。

技術能力

• 深入理解機器學習演算法（例如回歸、分類、叢集）和深度學習框架（例如CNN、RNN、Transformer）。

• 具備大型語言模型（LLMs）的專業知識，包括微調、提示工程和部署。

• 熟悉檢索增強生成（RAG）系統，包括向量資料庫（例如Pinecone、Weaviate）和嵌入模型。

• 具備構建AI代理的能力，支援任務自動化、推理和與外部API的互動。

• 了解基於圖的AI，包括圖神經網路（GNNs）、知識圖譜及其在推薦系統或網路分析中的應用。

• 精通資料處理、特徵工程以及文字、圖像或多模態資料的嵌入技術。

• 具備雲端平台（例如AWS、Google Cloud、Azure）和模型部署管道的經驗。

問題解決能力：能夠獨立分析並解決技術挑戰。

團隊合作：具備良好的溝通能力和跨部門協作經驗。

加分條件

• 具備自然語言處理（NLP）、電腦視覺或強化學習的專案經驗。

• 熟悉大數據工具（例如Hadoop、Spark）或容器技術（例如Docker、Kubernetes）。

• 發表過AI相關論文或擁有開源專案貢獻。

• 具備LLM框架（例如Hugging Face Transformers、OpenAI API）和代理框架（例如AutoGen、CrewAI）的實務經驗。

• 熟悉圖資料庫（例如Neo4j、ArangoDB）和圖演算法在AI應用中的知識。

Who We Are

We are seeking a passionate and technically skilled AI Engineer to join our team, responsible for designing, developing, and implementing artificial intelligence solutions. You will collaborate with cross-functional teams, leveraging machine learning, deep learning, and data analysis techniques to address complex problems and drive product innovation.

What You Will Do

• Design and develop AI models, including machine learning and deep learning algorithms, to address business needs.

• Clean, preprocess, and analyze large-scale datasets to ensure the quality of model input data.

• Integrate AI models into existing systems or applications, optimizing for performance and scalability.

• Collaborate with data scientists, software engineers, and product managers to define project goals and deliver results.

• Continuously monitor and improve deployed AI systems to ensure accuracy and reliability.

• Research the latest AI technologies and trends, proposing innovative applications for company products.

• Write clear technical documentation, detailing model development processes and deployment specifics.

• Develop and implement large language models (LLMs), retrieval-augmented generation (RAG) systems, AI agents, and graph-based AI solutions to enhance intelligent systems.

• Optimize LLMs for specific use cases, including fine-tuning, prompt engineering, and deployment in production environments.

• Design and build RAG pipelines to integrate external knowledge sources, improving model accuracy and contextual relevance.

• Create autonomous AI agents capable of task planning, decision-making, and multi-step reasoning.

• Leverage graph-based AI techniques, such as knowledge graphs and graph neural networks, to model complex relationships and enhance decision-making.

Who You Are

• Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field (Master’s or PhD preferred).

• Experience: At least 2-3 years of hands-on experience in AI, machine learning, or related fields.

• Technical Skills:

  • Programming Languages: Proficient in Python, with strong familiarity with relevant libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, LangChain, LlamaIndex).

  • Deep understanding of machine learning algorithms (e.g., regression, classification, clustering) and deep learning frameworks (e.g., CNN, RNN, Transformer).

  • Expertise in large language models (LLMs), including fine-tuning, prompt engineering, and deployment.

  • Experience with retrieval-augmented generation (RAG) systems, including vector databases (e.g., Pinecone, Weaviate) and embedding models.

  • Proficiency in building AI agents with capabilities in task automation, reasoning, and interaction with external APIs.

  • Knowledge of graph-based AI, including graph neural networks (GNNs), knowledge graphs, and their applications in recommendation systems or network analysis.

  • Strong skills in data processing, feature engineering, and embeddings for text, image, or multimodal data.

  • Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and model deployment pipelines.

• Problem-Solving: Ability to independently analyze and resolve technical challenges.

• Team Collaboration: Excellent communication skills and experience working across departments.

Bonus If You Have

• Project experience in natural language processing (NLP), computer vision, or reinforcement learning.

• Familiarity with big data tools (e.g., Hadoop, Spark) or container technologies (e.g., Docker, Kubernetes).

• Published AI-related papers or contributions to open-source projects.

• Hands-on experience with LLM frameworks (e.g., Hugging Face Transformers, OpenAI API) and agent frameworks (e.g., AutoGen, CrewAI).

• Knowledge of graph databases (e.g., Neo4j, ArangoDB) and graph algorithms for AI applications.