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.
• 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.
THE ROLE: We are currently looking for a highly experienced, dedicated Senior Software QA Engineer to join our team. In this capacity, you will play a key role in our company by leading the quality assurance efforts and maintaining the high quality of our software products. Your expertise in production processes, development of testing strategies, and staff supervision will be critical in ensuring our company’s success with outstanding software products. RESPONSIBILITIES: 1. Lead large complex projects and coordinate with teams to define and execute software quality assurance testing. 2. Drive the development of robust testing strategies and lead efforts in building a continuous integration and continuous delivery environment. 3. Review system requirements and identify testing strategies that align with the company’s quality goals. 4. Conduct thorough inspections of test cases, ensuring the functions of all systems operate properly. 5. Conduct AI test procedure, including designing evaluation metrics, curating validation dataset, analyzing individual model performance results and e2e AI application outcomes precisely. 6. Mentor and provide guidance to QA team members, and suggest improvements in automated testing processes. 7. Collaborate with engineering teams to thoroughly understand product architectures and come up with meaningful testing strategies. 8. Investigate customer problems referred by technical support and provide solutions.