亚马逊Senior AI Agent & Data Engineer, Amazon Global Selling -AIT
任职要求
基本任职资格 - 7+ years of data engineering experience - Experience with data modeling, warehousing and building ETL pipelines - Experience with SQL - Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS - Experience mentoring team members on best practices - Experience communicating with users, other technical teams, and management to collect requirements, describe data modeling decisions and data engineering strategy 优先任职资格 - Experience with big data technologies such a…
工作职责
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
• Design, fine-tune, and optimize AI models (CV, NLP, multi-modal, or hybrid) to meet real-world performance, latency, and accuracy requirements. • Develop and improve AI Agent algorithms, including reasoning, planning, decision-making, tool-use, and multi-agent collaboration strategies. • Build and maintain a Model Factory approach, enabling standardized model training, fine-tuning, evaluation, versioning, and deployment. • Conduct systematic experimentation on model architectures, hyperparameters, prompt strategies, and fine-tuning techniques. • Collaborate with data engineering teams to define data requirements for training, validation, and continuous model improvement. • Design evaluation frameworks and metrics to measure model and agent performance across accuracy, robustness, efficiency, and business impact. • Optimize inference performance for production environments, including latency, memory usage, and cost efficiency. • Research and apply state-of-the-art techniques in LLMs, foundation models, reinforcement learning, multi-agent systems, and multi-modal learning. • Translate business and product requirements into algorithmic strategies and technical designs. • Document model behavior, limitations, and improvement plans to support long-term maintainability and governance.
Lead the development and evaluation of advanced algorithms and models for content generation, especially image and video processing and generation to ensure high quality and engaging content Optimize and scale existing systems to handle large volumes of data and ensure high performanceConduct research and stay up-to-date with the latest advancements in AIGC, computer vision to ensure our solutions are cutting-edgeAnalyze performance and identify opportunities based on offline and online testing, develop, and deliver robust and scalable solutions, make direct impact to both user and advertisers experienceCollaborate with cross-functional teams, including product managers, designers, and other engineers, to define and execute on the ad content generation strategy.
• Capture business requirements, translate requirements into functional design, user stories, technical design, drive end to end integration testing, support data set up and issue remediation during UAT, manage development team activities, develop hypercare support model • Define and architect AI agents for Supply Chain use cases, using the right frameworks, multi-agent coordination, RAG, deployment, monitoring, and life cycle management. • Be hands on in quick proof of concepts development to demonstrate technical feasibility and implement enterprise grade Agentic Supply Chain solutions • Partner with Enterprise IT engineering, product, and research teams while evaluating LLMs, agentic frameworks, and NVIDIA’s own NeMo technologies. • Ensure integration with enterprise IT and Operations data sources and Industry’s best Agentic platforms with strong content security focus. • Drive architectural decisions across deployment models (on-prem, cloud, hybrid, containerized) to deliver scalable, reliable, and efficient solutions. • Lead design reviews, develop technical documentation, and guide developers in principles of architecture and code development. • Champion observability, monitoring, versioning, and telemetry to ensure trustworthy and auditable AI agents. • Influence Supply Chain Operations adoption of the platform by partnering with stakeholders across IT, supply chain and serve as a reference adopter providing feedback to strengthen NVIDIA’s ecosystem.