苹果AI Application Engineer

In this role, you will AI Solution Development: Design, develop, and deploy AI-driven applications, across domains, such as machine learning, NLP, and computer vision, addressing both business requirements and end-user needs. End to End Supply Chain Connectivity: Partner with supply chain experts to understand business goals and translate into clear technical specifications and solutions recommendation. Agent Design and Development: Design and implement intelligent agents within multi-agent systems, enabling real-time collaboration based on pre-defined goals, strategies, and data exchanges. Develop agent-based models to optimize decision-making and interactions. MCP Integration: Extend and integrate Multi-Agent Coordination Platforms(MCP) to optimize resource allocation, communication, and decision-making across multiple agents in shared environments. Data Management: Architect, design, build, and maintain data pipelines that connects across internal and external supply chain systems. Enforcing data governance and control is implemented at key matrix levels. Collaboration with Data Science Teams: Collaborate with data scientists to refine algorithms, optimize models, and enhance AI performance, focusing on model tuning, feature selection, and performance benchmarking. Testing and Validation: Conduct rigorous testing and validation of AI models, including unit testing, integration testing, and A/B testing, to ensure accuracy, reliability, and scalability before deployment. Monitoring and Maintenance: Monitor deployed AI models, track performance metrics, and implement continuous improvement strategies, including model re-training and updates based on real-world data and evolving business needs.

1. 内部数据梳理与标准化：全面梳理公司内部应用系统现状，明确数据流转路径及存储分布；主导内部结构化数据（日志、数据库数据等）与非结构化数据（内部文档、报表等）的收集、整理、清洗及格式化，搭建标准化数据体系； 2. 大模型应用与训练：基于多模态大模型技术，对梳理后的结构化、非结构化数据进行模型训练、调优及迭代，优化模型对内部数据的理解与分析能力，确保模型适配公司内部数据场景； 3. AI异常行为检测与告警智能体搭建：利用训练后的大模型，对内部数据访问、操作行为进行实时监测，落地预警智能体，开展异常行为数据分析，建立精准的告警机制，及时发现数据泄露风险并联动处置； 4. 自动化安全审计：基于AI技术框架，搭建内部自动化审计平台，提高审计效率； 5. 项目管理与交付：具备完整项目管理能力，牵头负责内部数据安全AI防护相关项目，制定项目计划、协调资源，带领内部员工及外部供应商推进项目实施，确保项目按时、按质完成交付，达成数据安全防护目标； 6. 技术跟踪与内部落地：及时跟踪业界AI最新技术、架构及应用案例，定期在内部开展技术分享，传递前沿动态；结合部门业务需求，探索AI技术在部门内部的落地实践，挖掘技术应用价值，助力部门工作效率与技术能力提升。

1. 现场技术落地：负责AIX的底座与产品在客户或业务现场的部署、调试与运行保障，快速响应并解决使用中的技术问题，确保产品平稳落地，让业务真正“用起来”； 2. 需求挖掘与反馈闭环：深入理解半导体业务场景，主动收集、梳理一线使用痛点和共性需求，持续为研发团队输送高质量反馈，参与需求优先级讨论，推动产品快速迭代； 3. 方案设计与Demo验证：针对不同业务场景，设计基于AIX的底座与产品的定制化解决方案，搭建Demo环境并进行概念验证，为产品功能演进和商业拓展提供技术支撑； 4. 赋能培训与最佳实践沉淀：为内部团队和业务用户提供技术培训、实操指导，编写应用案例、操作手册和最佳实践文档，降低使用门槛，扩大AIX的底座与产品的影响面； 5. 跨团队协同推动：与研发、产品、运维等兄弟团队紧密配合，作为技术接口人衔接前后端，参与产品路线图讨论，用一线应用视角帮助产品构建长期竞争力。

We are seeking a skilled developer to build production-grade AI applications, focusing on LLM-based agents and tool-using systems. You will integrate large language models (LLMs), retrieval-augmented generation (RAG), and external tools/APIs on GPU-accelerated stacks, enhancing agent frameworks for reliability, scalability, and safety. What You’ll Be Doing: • Design, implement, and deploy AI-powered features using LLMs, including autonomous and multi-agent workflows. • Build agent toolchains, including planning, tool/function calling, memory management, RAG integration, and enterprise API connectivity. • Enhance agent frameworks with custom planners, routers, concurrency control, state management, and retry mechanisms. • Develop evaluation and observability systems to monitor agent performance (success rates, tool-call accuracy, latency, cost, traces). • Implement safety and compliance measures, including content filtering, PII handling, and policy enforcement using guardrail frameworks. • Optimize inference pipelines for GPU performance, latency, and cost; deploy via microservices and APIs. • Manage CI/CD, containerization, and deployment; maintain monitoring, logging, and alerting; and produce clear documentation.