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Job Description: AI Development Expert (Intelligent Process Automation)
1. Document Intelligence: Utilizing advanced OCR technologies (e.g., Tesseract, Azure Form Recognizer) to automatically parse and extract critical information from diverse documents (contracts, invoices), structuring the output for storage in databases.
2. Intelligent Content Generation: Integrating state-of-the-art Large Language Models (LLMs) such as GPT-4, Claude, or local models to automate the drafting of precise email responses. This will be based on the structured document data, business rules, and contextual understanding.
3. End-to-End System Integration: Architecting and developing robust, scalable automation pipelines. You will connect document management systems, databases, and communication platforms (e.g., Gmail API) using modern API frameworks like FastAPI to create a seamless workflow.
Required Qualifications & Experience:
1. Technical Expertise: Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow) is essential, with proven hands-on experience in OCR and NLP projects.
2. Cloud & ML Platforms: Demonstrable expertise in at least one major cloud AI service (AWS SageMaker, Google Vertex AI, or Azure Machine Learning) for building, training, and deploying models.
3. Full-Stack AI Development: Solid experience in database integration (SQL/NoSQL) and API development, with a proven ability to deliver complete, production-grade solutions, not just proof-of-concepts.
4. Bonus Qualifications: Practical experience with IoT data processing, robotic scheduling systems, or Warehouse Management System (WMS) development is a significant advantage.
Essential AI Skills & Mindset:Beyond technical prowess, we are looking for a candidate with a strong analytical and product-oriented mindset.
1. Data-Centric Thinking: A fundamental understanding that model performance is rooted in data quality. You must be skilled in data preprocessing, augmentation, and MLOps principles for robust model lifecycle management.
2. Problem-Solving & Evaluation: The ability to frame business problems with an AI lens, select appropriate models (from traditional ML to modern LLMs), and rigorously evaluate their performance using relevant metrics.
3. Production & Scalability Focus: A keen understanding of the challenges in moving from a prototype to a live production environment, including considerations for latency, monitoring, and maintenance.
4. Collaboration & Communication: Excellent ability to explain complex AI concepts to non-technical stakeholders and collaborate effectively with cross-functional teams.
5. Continuous Learning: A passionate, self-motivated learner who stays current with the rapidly evolving AI landscape and is eager to experiment with new technologies.
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招聘職位：AI開發(fā)專家（智能流程自動(dòng)化方向）
1. 文檔智能處理： 運(yùn)用先進(jìn)的OCR技術(shù)（如Tesseract、Azure Form Recognizer）自動(dòng)解析各類文檔（如合同、發(fā)票），提取關(guān)鍵信息，并轉(zhuǎn)化為結(jié)構(gòu)化數(shù)據(jù)存入數(shù)據(jù)庫。
2. 智能內(nèi)容生成： 集成前沿的大型語言模型（如GPT-4、Claude或本地模型），基于已結(jié)構(gòu)化的文檔數(shù)據(jù)、業(yè)務(wù)規(guī)則和上下文語境，自動(dòng)生成或起草精準(zhǔn)的郵件回復(fù)。
3. 端到端系統(tǒng)集成： 設(shè)計(jì)并開發(fā)穩(wěn)健、可擴(kuò)展的自動(dòng)化流程。通過使用FastAPI等現(xiàn)代API框架，連接文檔管理系統(tǒng)、數(shù)據(jù)庫和通信平臺（如ouklook API），打造無縫的業(yè)務(wù)流。
必備資質(zhì)與經(jīng)驗(yàn)：
1. 技術(shù)專長： 必須精通Python及深度學(xué)習(xí)框架（PyTorch或TensorFlow），并具備扎實(shí)的OCR和NLP項(xiàng)目實(shí)戰(zhàn)經(jīng)驗(yàn)。
2. 云平臺與ML服務(wù)： 熟練掌握至少一家主流云平臺的AI服務(wù)，能夠用于模型的構(gòu)建、訓(xùn)練與部署。
3. 全棧AI開發(fā)能力： 具備扎實(shí)的數(shù)據(jù)庫（SQL/NoSQL）集成和API開發(fā)經(jīng)驗(yàn)，擁有交付完整、生產(chǎn)級解決方案（而不僅僅是概念驗(yàn)證）的成功案例。
4. 加分項(xiàng)： 具備物聯(lián)網(wǎng)數(shù)據(jù)處理、機(jī)器人調(diào)度系統(tǒng)或倉儲(chǔ)管理系統(tǒng)（WMS）的實(shí)際開發(fā)經(jīng)驗(yàn)者優(yōu)先。
關(guān)鍵的AI技能與思維模式：除了技術(shù)能力，我們更期待您具備出色的分析能力和產(chǎn)品思維。
1. 數(shù)據(jù)驅(qū)動(dòng)思維： 深刻理解模型性能根源于數(shù)據(jù)質(zhì)量，精通數(shù)據(jù)預(yù)處理、數(shù)據(jù)增強(qiáng)，并了解MLOps理念以確保模型生命周期的穩(wěn)健管理。
2. 問題解決與評估能力： 善于從AI視角界定業(yè)務(wù)問題，能選擇合適的模型（從傳統(tǒng)機(jī)器學(xué)習(xí)到現(xiàn)代大語言模型），并使用相關(guān)指標(biāo)嚴(yán)格評估其性能。
3. 生產(chǎn)與規(guī)模化意識： 深刻理解從原型到生產(chǎn)環(huán)境所面臨的挑戰(zhàn)，包括對延遲、監(jiān)控和維護(hù)等方面的考量。
4. 協(xié)作與溝通能力： 出色的溝通能力，能夠向非技術(shù)背景的同事清晰地解釋復(fù)雜的AI概念，并能高效地與跨職能團(tuán)隊(duì)協(xié)作。
5. 持續(xù)學(xué)習(xí)熱情： 對AI領(lǐng)域充滿熱情，具備強(qiáng)大的自驅(qū)力，能主動(dòng)追蹤快速變化的技術(shù)趨勢，并樂于嘗試新技術(shù)。