Agentic AI Engineer, AVP

Agentic AI Engineer Position ID: P946333 Location: Hangzhou, China Employment Type: Full-time Role Summary We are seeking an experienced Agentic AI Engineer to design, build, and evolve enterprise-grade Agentic AI platforms and solutions supporting investment management, investment research, investment performance, and related daily operations. The successful candidate will combine strong hands-on engineering capabilities with architectural judgment. The role will define and implement reusable Agentic AI capabilities across prompt engineering, memory, knowledge management, retrieval-augmented generation, agent orchestration, tool integration, evaluation, observability, security, and governance. The individual will work closely with platform engineering, data engineering, application teams, enterprise architecture, information security, model risk, and business stakeholders to transform business requirements into scalable, secure, explainable, and production-ready AI solutions. Key Responsibilities Build production-grade Agentic AI solutions that automate and augment investment performance, investment research, financial analytics, document processing, and other operational workflows. Develop multi-agent and workflow-based solutions capable of decomposing complex business questions, retrieving relevant information, invoking approved tools and APIs, synthesizing results, and producing traceable outputs. Design and implement Retrieval-Augmented Generation solutions using structured and unstructured enterprise data, vector search, metadata filtering, document parsing, semantic retrieval, reranking, and source attribution. Integrate large language models and agent frameworks with enterprise applications, data platforms, databases, APIs, model gateways, and approved cloud services. Build reusable Agentic AI components and patterns, such as research agents, financial analytics agents, document-processing agents, evaluation agents, workflow agents, and governed tool-execution services. Establish engineering standards and guardrails for prompt versioning, context management, memory, agent state, tool permissions, error handling, fallback behavior, and deterministic workflow controls. Implement comprehensive evaluation frameworks covering answer quality, groundedness, retrieval relevance, tool-selection accuracy, task completion, latency, cost, safety, and regression testing. Deliver explainable and auditable solutions by preserving source references, generated queries, tool-call traces, execution history, model and prompt versions, and relevant decision records. Design solutions with appropriate identity, authentication, authorization, data access, encryption, logging, monitoring, and audit controls. Partner with Platform Engineering to align infrastructure, deployment, observability, CI/CD, secrets management, networking, resiliency, and production-support capabilities with solution requirements. Collaborate with data engineering teams on governed data ingestion, metadata management, data quality, schema evolution, lifecycle management, and Lakehouse integration. Review technical designs and code to ensure alignment with architectural principles, engineering standards, performance expectations, security requirements, and responsible AI practices. Work with enterprise architecture, security, compliance, privacy, model risk, and AI governance teams to support required reviews and production approvals. Diagnose production issues involving model behavior, retrieval, prompts, workflows, tools, application code, open-source libraries, and platform integrations. Create and maintain architecture diagrams, technical specifications, design guidelines, reusable implementation templates, operational runbooks, and architectural decision records. Evaluate emerging models, agent protocols, frameworks, and development tools, and recommend their controlled adoption based on measurable business and engineering value. Mentor engineers and contribute to engineering best practices, technical knowledge sharing, and the development of the broader Agentic AI engineering community. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Financial Engineering, Mathematics, or a related technical discipline. Strong professional software-engineering experience, with demonstrated delivery of enterprise applications, AI platforms, data platforms, or commercially deployed technology products. Hands-on experience designing and developing Generative AI or Agentic AI solutions using large language models. Strong programming skills in Python ; experience with Java, TypeScript, or another enterprise programming language is beneficial. Practical experience with one or more Agentic AI or LLM application frameworks, such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel , or comparable technologies. Experience with key Agentic AI patterns, including planning, routing, reflection, tool/function calling, workflow orchestration, memory, multi-agent collaboration, and human-in-the-loop review. Hands-on experience implementing RAG , semantic search, embeddings, vector databases, document ingestion, metadata filtering, reranking, and grounded response generation. Experience integrating LLM applications with structured data, databases, REST APIs, enterprise services, and data-processing pipelines. Understanding of modern Agentic AI interoperability concepts and protocols, such as Model Context Protocol (MCP) or comparable tool and context integration mechanisms. Experience with prompt engineering, structured outputs, context-window management, prompt and model versioning, and automated evaluation. Strong understanding of distributed systems, scalability, performance, resiliency, observability, and secure application design. Experience developing solutions in at least one major public-cloud environment, such as Azure, AWS, or GCP . Familiarity with cloud AI and data services, such as Azure AI Foundry/Azure Machine Learning, AWS Bedrock/SageMaker, Databricks, Snowflake, or comparable platforms. Experience with CI/CD, containerization, source control, automated testing, infrastructure integration, and production deployment. Understanding of enterprise data governance, metadata management, identity and access control, privacy, security, compliance, and audit requirements. Strong analytical and problem-solving skills, with the ability to convert ambiguous business needs into practical technical designs and working solutions. Strong written and verbal communication skills, with the ability to collaborate effectively across business, engineering, architecture, security, risk, and global stakeholder groups. Ability to communicate professionally in both English and Mandarin Chinese . Preferred Qualifications Experience delivering AI, data, or analytics solutions within asset management, investment management, banking, insurance, or another regulated industry. Knowledge of investment research, portfolio management, investment performance, financial instruments, market data, reference data, risk management, or financial reporting. Experience building natural-language-to-SQL, financial analytics, research-assistant, document-intelligence, or workflow-automation solutions. Experience with Lakehouse and large-scale data technologies, including Apache Iceberg, Spark, Databricks, Snowflake, PostgreSQL, or similar platforms. Experience with model evaluation, model validation, responsible AI controls, content safety, red teaming, and AI governance processes. Experience implementing production observability for LLM applications, including prompt and trace monitoring, quality metrics, token and cost monitoring, latency measurement, and feedback loops. Familiarity with AI-assisted software-development tools such as GitHub Copilot or equivalent development environments. Experience guiding other engineers, reviewing architecture and code, and influencing technical decisions across multiple teams. Relevant cloud, data, AI, or financial-industry certifications are beneficial. Key Competencies Strong hands-on engineering and delivery mindset Architectural thinking with pragmatic execution Curiosity and continuous learning Ownership and accountability Collaboration across global and cross-functional teams Attention to security, governance, quality, and operational resilience Ability to balance innovation with the control requirements of a regulated enterprise Success Measures Success in this role will be demonstrated through: Delivery of secure, reusable, and production-ready Agentic AI platform capabilities. Successful implementation of Agentic AI solutions that improve the speed, quality, consistency, and traceability of business operations. Adoption of common engineering patterns and reusable components across application teams. Measurable improvements in solution quality, groundedness, reliability, latency, and operational supportability. Effective compliance with enterprise architecture, security, data governance, responsible AI, and audit requirements. Clear technical documentation and effective collaboration with engineering, business, architecture, and governance stakeholders. About State Street Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success. We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future. 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