AI Engineer AI工程师 11k-19k

Responsibilities 具体职责
1. Development of AI intelligent agents；
Company AI Culture Training；
User AI tool training guidance；
Assist the product manager in identifying company process pain points and implement them using AI tools；
2. Model Development & Training:
Responsible for end-to-end development of machine learning/deep learning models, including data collection & cleaning, feature engineering, model training, tuning, and evaluation.
Stay updated with the latest advancements in academia and industry (e.g., Large Language Models, AIGC, Multimodal Learning) and explore their application in company business scenarios.
Design and implement suitable AI solutions for specific business problems.
3. Engineering & Deployment:
Productize experimental models, building high-performance, highly available online inference services.
Use technologies like Docker, Kubernetes for containerizing and managing models.
Optimize model inference speed and resource consumption to meet production environment requirements.
4. Data Processing & Architecture:
Collaborate with data engineers to build and maintain efficient, reliable data pipelines.
Process large-scale structured and unstructured data to fuel model training.
5. Collaboration & Communication:
Work closely with product managers and business teams to deeply understand requirements and clearly communicate complex technical solutions to non-technical colleagues.
Write high-quality technical documentation to ensure project maintainability and reproducibility.

1. AI智能体开发与文化建设
开发AI智能体（AI Agent）：负责设计并实现人工智能智能体系统
AI文化培训：负责公司内部的AI文化培育与推广工作，提升全员的AI认知和应用意识
提供工具培训：为用户提供AI工具（如ChatGPT、Copilot等）的使用培训和指导，赋能员工利用AI提升工作效率
业务洞察与AI解决方案设计：协助产品经理分析公司业务流程，识别可被AI优化或自动化的痛点，针对具体的业务问题，设计并实施合适的AI解决方案
2. 模型开发与训练
端到端模型开发：负责机器学习/深度学习模型的端到端开发，包括数据收集与清洗、特征工程、模型训练、调优及评估
技术前瞻与应用探索：持续跟踪学术界与工业界（如大语言模型、AIGC、多模态学习）的最新进展，并探索其在公司业务场景中的应用可能性
3. 工程实现与部署
模型产品化：将实验模型产品化，构建高性能、高可用的在线推理服务
容器化与运维：使用Docker、Kubernetes等技术进行模型的容器化部署与管理
性能优化：优化模型推理速度与资源消耗，以满足生产环境要求
4. 数据处理与架构
构建数据管道：与数据工程师协作，构建并维护高效、可靠的数据管道
处理大规模数据：处理大规模的结构化和非结构化数据，为模型训练提供支持
5. 协作沟通与文档编写
跨团队协作：与产品经理、业务团队紧密合作，深入理解需求，并向非技术同事清晰地阐释复杂的技术方案
编写技术文档：撰写高质量的技术文档，确保项目的可维护性和可复现性

Requirements 岗位要求
Education: Bachelor's degree or higher in Computer Science, Artificial Intelligence, Mathematics, Statistics, or a related field.
Programming Skills: Proficient in Python, with solid programming fundamentals and good coding style.
AI/ML Fundamentals: Solid theoretical foundation in machine learning/deep learning, familiar with common models (e.g., CNN, RNN, Transformer, GAN) and algorithms.
Frameworks & Tools: Proficient in at least one mainstream deep learning framework, such as PyTorch or TensorFlow.
Engineering Experience: Hands-on project experience, capable of independently handling the full workflow from data preparation to model deployment.
Problem-Solving: Excellent analytical and problem-solving skills, passionate about challenging tasks.
Teamwork: Strong communication skills and team spirit.
教育背景
本科及以上学历，计算机科学、人工智能、数学、统计学或相关领域
编程技能
熟练掌握 Python，具备扎实的编程基础和良好的代码风格
AI/ML 基础
具备扎实的机器学习/深度学习理论基础，熟悉常见模型（如 CNN、RNN、Transformer、GAN）和算法
框架与工具
至少熟练掌握一种主流深度学习框架，如 PyTorch 或 TensorFlow
工程经验
拥有实际动手项目经验，能够独立完成从数据准备到模型部署的完整流程
解决问题能力
出色的分析能力和解决问题能力，对富有挑战性的任务充满热情
团队合作
较强的沟通能力和团队合作精神