苹果AI Application Engineer
任职要求
Minimum Qualifications • - Bachelor's degree in Computer Science, Engineering, or related field with a minimum of 5+ years of relevant industry experience in AI application development, machine learning, or software engineering • - Strong understanding of generative AI models(e.g. LLMs such as GPT, BERT) and their application in real-world solutions, such as chatbots, NLP applications, and content generation • - Excellent software engineering skills, with expertise in modular, object-oriented design, and familiarity with industry-standard development processes. Proficiency in Python, PHP, Java, Git preferred. • - Comfort with ambiguity, with the ability to structure complex analysis and drive insights through data exploration and strategy research. Preferred Qualifications • - 3+ years of hands-on experience developing multi-agent systems or agent-based modeling, with experience in frameworks like MAS platforms, NetLogo, …
工作职责
• 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. • - 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. • - 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.
• Work as a system application engineer, providing support to our server and data-center customer engineering teams. • Deliver product specifications and key build information, guiding customers in server product development and verification. • Collaborate with customer engineering teams on server product development, testing, and debugging to ensure customer success.
• Understand business scenarios and design targeted data acquisition solutions, ensuring data is relevant, high-quality, and aligned with project goals. • Architect, design, and maintain enterprise-grade databases, data warehouses, and lakehouse systems to support analytical, operational, and AI workloads. • Model and optimize schema design, storage layouts, data partitioning, clustering, and indexing strategies for large-scale datasets. • Implement and maintain ETL/ELT pipelines feeding data warehouses (e.g., Snowflake, BigQuery, Redshift, Databricks, or open-lakehouse environments). • Design, collect, and maintain high-quality datasets for AI inferencing and LLM model optimization, fine-tuning, and testing, ensuring data is formatted and preprocessed to meet model requirements. • Collaborate with AI application engineers to understand model performance requirements and translate them into targeted data collection and preparation strategies. • Develop and implement automated data pipelines for efficient data processing, including data cleaning, labeling, augmentation, and transformation. • Proactively identify data gaps based on model performance metrics, design solutions to acquire, clean, and optimize data for enhanced model accuracy and efficiency. • Build, clean, and manage diverse data sources, ensuring compliance with data security and privacy standards. • Conduct exploratory data analysis to discover data patterns, anomalies, and optimization opportunities, directly impacting model performance. • Continuously learn and adapt to the latest advancements in data engineering, AI, and large language model (LLM) technologies.
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.
AI Agent Engineering • Design, develop, and deploy production-grade AI agent systems, including multi-agent orchestration, tool-use frameworks, memory management, and API integration — ensuring reliability, scalability, and maintainability • Build and optimize Retrieval-Augmented Generation (RAG) pipelines: document ingestion, chunking strategy, embedding, vector search, and re-ranking to maximize LLM grounding quality • Support LLM adaptation to WWGS business domains through prompt engineering, context injection, fine-tuning signal curation, and systematic prompt evaluation frameworks • Develop automated knowledge base construction and real-time data access capabilities (Data Agent, MCP server/client) to connect AI agents with live business data • Design and implement LLM evaluation pipelines to systematically assess agent output quality, hallucination risk, and business impact Data Engineering • Design and implement end-to-end data pipelines (batch and streaming) for data collection, transformation, and storage — supporting both AI application and analytics use cases • Build and maintain integration layer data models that serve as a unified, AI-ready data foundation across WWGS domains • Develop automated data quality monitoring, alerting, and observability tooling to ensure pipeline reliability and data trustworthiness • Integrate multi-source data (seller behavior, transaction logs, off-platform signals, AI outputs) into a coherent, governed data layer • Establish data standardization and governance policies ensuring consistency, accuracy, and compliance across AI and BI consumption layers Technical Leadership • Provide technical guidance on AI-data architecture decisions; define best practices for the team's AI agent and data engineering stack • Collaborate cross-functionally with Product, Operations, and Science teams to translate business requirements into scalable technical solutions • Mentor junior engineers and conduct design reviews; raise the technical bar across the team