JD数据
{
"schema_version": "via_ai_product_manager_jobs_v1",
"data_type": "ai_product_manager_jobs",
"job": {
"job_title": "AI 应用开发",
"job_level": "高级",AI生成
"work_category": "全职",AI生成
"department": "未披露",
"location": "杭州·滨江区·长河",
"work_mode": "现场办公",AI生成
"job_category": "AI应用软件工程师"AI生成
},
"company": {
"company_name": "美国道富",
"company_industry": "互联网金融",
"company_size": "10000人以上",
"company_website": "未披露",
"company_description": "State Street Corporation (NYSE: STT) is a provider of financial services to institutional investors, including investment servicing, investment management and investment research and trading. With US$36.7 trillion in assets under custody and US$3.5 trillion in assets under management globally, State Street operates in 30 countries and more than 100 geographic markets worldwide and employs more than 40,000 employees worldwide. State Street Technology (Zhejiang) Co., Ltd is a wholly owned subsidiary of State Street Corporation, is committed to research and development of global financial services in the information technology and financial asset services. The company has more than 4,300 outstanding talents. Since the beginning of 2008 company carried out the financial asset services as our key development business. State Street Hangzhou has become the focus of the strategic development of the company in the Asia Pacific region, or the financial services center of the company's global and Asia Pacific. \n\n美国道富集团成立于1792年，是一家托管银行和资产管理公司，托管的资产高达36.7万亿美元，管理着超过3.5万亿美元的资产（数据截止2022年12月31日）。总部位于美国麻萨诸塞州波士顿，共有40,000多名员工，在30个国家和地区设有分支机构，业务网络遍及全球100多个全球金融市场，在全球金融资产服务业名列前茅。 道富信息科技（浙江）有限公司（以下简称“道富杭州”）是美国道富在中国的全资子公司，致力于研究和开发全球化金融服务中的信息技术和提供金融资产服务。目前公司拥有优秀人才4,300多名。自2008年初开展的金融资产服务也是我们公司的重点发展之一。道富杭州已经成为美国道富公司在亚太区的战略发展重点，还是道富公司全球、亚太区的金融服务中心。"
},
"compensation": {
"salary_range": {
"min": 30000,
"max": 50000,
"currency": "元",
"cycle": "月"
},
"benefits": "带薪年假 | 定期体检 | 生日福利 | 综合补贴 | 高温补贴 | 补充医疗保险 | 年终奖 | 节日福利 | 五险一金 | 加班补助"
},
"requirements": {
"working_years": 5,
"degree": "本科",
"language": "英文",AI生成
"visa_support": "未披露",
"responsibilities": "AI 应用开发\n大模型交互\nAgent开发,要求英语能作为工作语言\nWho we are looking for\nWe are seeking a skilled Gen AI Developer with deep hands-on engineering expertise and proven leadership experience building production-grade AI systems. The ideal candidate combines strong software engineering fundamentals with modern AI/ML capabilities (specifically LLM-based applications), and can drive teams to deliver reliable, secure, and scalable AI products that create measurable business impact.\nIn this role, you will own the end-to-end design and delivery of AI solutions—from problem framing and model selection to deployment, monitoring, and continuous improvement. You will lead & work with engineering teams on architecture and implementation choices, establish best practices for MLOps and AI governance, and ensure solutions meet performance, reliability, privacy, and compliance requirements while aligning with business strategy.\nWhat you will be responsible for\n· Lead end-to-end development of AI features and services (LLM, NLP, CV, predictive ML) from prototyping to production, aligned to clear product outcomes and KPIs.\n· Review and govern AI solution designs to ensure correctness, robustness, security, and responsible-AI compliance (bias, toxicity, hallucination risk, data leakage).\n· Partner with product and engineering teams to translate business problems into AI solution approaches (build vs buy, model choice, data needs, evaluation strategy) and executable delivery plans.\n· Lead and scale high-performing AI engineering teams delivering multiple initiatives in parallel (AI-enabled applications, internal platforms, and developer tooling).\n· Identify AI-specific risks (data quality, drift, adversarial use, hallucinations, latency/cost) and implement mitigation strategies throughout the delivery lifecycle.\n· Promote automation and engineering excellence (CI/CD for ML, automated evaluation, red-teaming, monitoring/alerting, and cost/performance optimization) to improve quality and velocity.\n· Collaborate closely with product, data, security, legal/compliance, and global engineering teams to align on AI use cases, constraints, and delivery expectations.\n· Coordinate architecture and delivery plans across multiple parallel AI initiatives (e.g., GenAI assistants, search/RAG, personalization, fraud/risk models, automation).\n· Work with platform teams to ensure reusable AI components and consistent patterns (prompt orchestration, retrieval, model gateways, feature management, observability) across the portfolio.\n· Act as a trusted technical advisor on AI strategy, platform choices, vendor/partner decisions, and responsible AI governance.\n· Explore, evaluate, and introduce AI technologies where they add clear business value (models, frameworks, orchestration tools, data platforms), balancing performance, cost, and risk.\n· Continuously improve AI development lifecycle practices (data/versioning, evaluation, deployment, monitoring, incident response) and governance models.\n· Mentor AI engineers and applied scientists, strengthening engineering rigor, model quality, and responsible-AI maturity.\nWhat we value\n· Strong sense of ownership and accountability for AI outcomes, production reliability, and responsible-AI risk management.\n· Ability to think strategically about AI adoption while remaining hands-on when needed (design reviews, critical debugging, and technical decision making).\n· Pragmatic problem-solving skills with a focus on shipping AI capabilities safely, iterating quickly with evidence, and delivering business value.\n· A collaborative mindset and the ability to influence across product, engineering, data, and risk/compliance stakeholders without relying on formal authority.\nEducation & Preferred Qualifications\n· Bachelor’s degree or above in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).\n· 8+ years of experience in software engineering, including significant leadership experience delivering AI/ML systems in production at scale.\n· Strong software engineering fundamentals (distributed systems, APIs, data structures) and proficiency with modern AI development stacks (Python plus one of Java/Go/C++), including strong code quality practices.\n· Proven experience building and operating AI/ML workloads on public cloud platforms (AWS/Azure/GCP), including scalable model training and low-latency inference deployment.\n· Solid experience with data platforms and pipelines (e.g., Databricks, Snowflake, Spark) and familiarity with vector search / embeddings for retrieval-based AI solutions.\n· Deep understanding of modern engineering practices and MLOps (CI/CD, testing, experiment tracking, model/version management, monitoring, incident management).\n· Experience working in global or distributed teams across different regions and time zones.\n· Strong communication skills in English and Chinese, including the ability to present complex AI topics and decisions to senior stakeholders.\n· Demonstrated experience with Responsible AI practices and governance (privacy, security, bias/fairness, content safety, auditability, and regulatory compliance).\n· Hands-on experience with LLM/GenAI application development (prompting, RAG, Agents, tool/function calling), model evaluation, and production observability.",
"requirements_text": "· Bachelor’s degree or above in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).\n· 8+ years of experience in software engineering, including significant leadership experience delivering AI/ML systems in production at scale.\n· Strong software engineering fundamentals (distributed systems, APIs, data structures) and proficiency with modern AI development stacks (Python plus one of Java/Go/C++), including strong code quality practices.\n· Proven experience building and operating AI/ML workloads on public cloud platforms (AWS/Azure/GCP), including scalable model training and low-latency inference deployment.\n· Solid experience with data platforms and pipelines (e.g., Databricks, Snowflake, Spark) and familiarity with vector search / embeddings for retrieval-based AI solutions.\n· Deep understanding of modern engineering practices and MLOps (CI/CD, testing, experiment tracking, model/version management, monitoring, incident management).\n· Experience working in global or distributed teams across different regions and time zones.\n· Strong communication skills in English and Chinese, including the ability to present complex AI topics and decisions to senior stakeholders.\n· Demonstrated experience with Responsible AI practices and governance (privacy, security, bias/fairness, content safety, auditability, and regulatory compliance).\n· Hands-on experience with LLM/GenAI application development (prompting, RAG, Agents, tool/function calling), model evaluation, and production observability.",
"skills": [AI生成
"Linux/DevOps/CI-CD",
"Prompt工程",
"RAG与检索增强",
"云平台与云计算",
"大模型原理与能力边界",
"工具调用/MCP",
"模型/Agent评测体系",
"模型训练与算法优化",
"沟通表达与方案汇报",
"结果导向与Ownership",
"编程与代码实现",
"跨部门/跨团队协作",
"部署上线与环境配置"
],
"preferred_qualifications": "Education & Preferred Qualifications",
"job_highlights": "5-10年 | 本科"
},
"contact": {
"recruiter_name": "黄女士",
"contact_methods": [
{
"type": "apply_link",
"value": "https://www.zhipin.com/job_detail/b2038a69bdad258d0ndz3d69FltV.html"
}
]
},
"posting": {
"posting_id": "b2038a69bdad258d0ndz3d69FltV",
"post_date": "",
"hire_time": "未披露",
"update_date": "20260915",
"job_url": "https://www.zhipin.com/job_detail/b2038a69bdad258d0ndz3d69FltV.html"
},
"fulltext": "AI 应用开发\n大模型交互\nAgent开发，要求英语能作为工作语言\nWho we are looking for\nWe are seeking a skilled Gen AI Developer with deep hands-on engineering expertise and proven leadership experience building production-grade AI systems. The ideal candidate combines strong software engineering fundamentals with modern AI/ML capabilities (specifically LLM-based applications), and can drive teams to deliver reliable, secure, and scalable AI products that create measurable business impact.\nIn this role, you will own the end-to-end design and delivery of AI solutions—from problem framing and model selection to deployment, monitoring, and continuous improvement. You will lead & work with engineering teams on architecture and implementation choices, establish best practices for MLOps and AI governance, and ensure solutions meet performance, reliability, privacy, and compliance requirements while aligning with business strategy.\nWhat you will be responsible for\n· Lead end-to-end development of AI features and services (LLM, NLP, CV, predictive ML) from prototyping to production, aligned to clear product outcomes and KPIs.\n· Review and govern AI solution designs to ensure correctness, robustness, security, and responsible-AI compliance (bias, toxicity, hallucination risk, data leakage).\n· Partner with product and engineering teams to translate business problems into AI solution approaches (build vs buy, model choice, data needs, evaluation strategy) and executable delivery plans.\n· Lead and scale high-performing AI engineering teams delivering multiple initiatives in parallel (AI-enabled applications, internal platforms, and developer tooling).\n· Identify AI-specific risks (data quality, drift, adversarial use, hallucinations, latency/cost) and implement mitigation strategies throughout the delivery lifecycle.\n· Promote automation and engineering excellence (CI/CD for ML, automated evaluation, red-teaming, monitoring/alerting, and cost/performance optimization) to improve quality and velocity.\n· Collaborate closely with product, data, security, legal/compliance, and global engineering teams to align on AI use cases, constraints, and delivery expectations.\n· Coordinate architecture and delivery plans across multiple parallel AI initiatives (e.g., GenAI assistants, search/RAG, personalization, fraud/risk models, automation).\n· Work with platform teams to ensure reusable AI components and consistent patterns (prompt orchestration, retrieval, model gateways, feature management, observability) across the portfolio.\n· Act as a trusted technical advisor on AI strategy, platform choices, vendor/partner decisions, and responsible AI governance.\n· Explore, evaluate, and introduce AI technologies where they add clear business value (models, frameworks, orchestration tools, data platforms), balancing performance, cost, and risk.\n· Continuously improve AI development lifecycle practices (data/versioning, evaluation, deployment, monitoring, incident response) and governance models.\n· Mentor AI engineers and applied scientists, strengthening engineering rigor, model quality, and responsible-AI maturity.\nWhat we value\n· Strong sense of ownership and accountability for AI outcomes, production reliability, and responsible-AI risk management.\n· Ability to think strategically about AI adoption while remaining hands-on when needed (design reviews, critical debugging, and technical decision making).\n· Pragmatic problem-solving skills with a focus on shipping AI capabilities safely, iterating quickly with evidence, and delivering business value.\n· A collaborative mindset and the ability to influence across product, engineering, data, and risk/compliance stakeholders without relying on formal authority.\nEducation & Preferred Qualifications\n· Bachelor’s degree or above in Computer Science, Machine Learning, Data Science, or a related field (or equivalent practical experience).\n· 8+ years of experience in software engineering, including significant leadership experience delivering AI/ML systems in production at scale.\n· Strong software engineering fundamentals (distributed systems, APIs, data structures) and proficiency with modern AI development stacks (Python plus one of Java/Go/C++), including strong code quality practices.\n· Proven experience building and operating AI/ML workloads on public cloud platforms (AWS/Azure/GCP), including scalable model training and low-latency inference deployment.\n· Solid experience with data platforms and pipelines (e.g., Databricks, Snowflake, Spark) and familiarity with vector search / embeddings for retrieval-based AI solutions.\n· Deep understanding of modern engineering practices and MLOps (CI/CD, testing, experiment tracking, model/version management, monitoring, incident management).\n· Experience working in global or distributed teams across different regions and time zones.\n· Strong communication skills in English and Chinese, including the ability to present complex AI topics and decisions to senior stakeholders.\n· Demonstrated experience with Responsible AI practices and governance (privacy, security, bias/fairness, content safety, auditability, and regulatory compliance).\n· Hands-on experience with LLM/GenAI application development (prompting, RAG, Agents, tool/function calling), model evaluation, and production observability."
}