AI Agent Application Engineer(Finance)

Job Responsibilities:

1. Responsible for designing, developing, and deploying enterprise-level Finance AI Agents, transforming finance processes, data logic, and analytical methods into reusable Agents, Skills, Workflows, and data capabilities.
2. Combine LLMs, Agent technologies, automation tools, and enterprise data platforms to convert manual finance analysis tasks into stable, accurate, traceable, and auditable intelligent applications.
3. Design and develop core Finance AI Agent capabilities, including Agent architecture, Skills, Workflow orchestration, knowledge bases, tool calling, context management, memory management, and human-in-the-loop approval.
4. Collaborate with Finance Analysts and SMEs to analyze data sources, SOPs, calculation rules, and deliverables, and transform finance processes into standardized, configurable, and reusable Agent Skills.
5. Integrate Excel, databases, data warehouses, Power BI, enterprise APIs, and public data sources to build data acquisition, cleansing, transformation, validation, aggregation, and metric calculation capabilities.
6. Build enterprise-grade AI engineering capabilities, including permission control, data isolation, result validation, approval workflows, exception handling, retry mechanisms, checkpoint recovery, execution auditing, data lineage, and observability.
7. Work with Finance, Data, and Engineering teams to define actions, owners, timelines, deliverables, and acceptance criteria, and drive MVP, pilot, and production delivery.

Job Requirements:

1. Experience in designing, developing, and deploying enterprise-level AI Agents or intelligent applications, with the ability to transform complex business processes into reusable Agent capabilities.
2. Familiarity with LLMs, Agent technologies, automation tools, and enterprise data platforms, with the ability to convert manual analysis tasks into intelligent, auditable applications.
3. Understanding of finance processes, data logic, and analytical methods, with the ability to collaborate with Finance Analysts and SMEs to analyze data sources, SOPs, calculation rules, and deliverables.
4. Data platform development and integration skills, with familiarity with common data sources and tools such as Excel, databases, data warehouses, Power BI, enterprise APIs, and public data sources.
5. Hands-on experience in enterprise-grade AI engineering, including permission control, data isolation, result validation, approval workflows, exception handling, retry mechanisms, checkpoint recovery, execution auditing, data lineage, and observability.
6. Cross-functional project collaboration and delivery capability, with the ability to work with Finance, Data, and Engineering teams to define actions, owners, timelines, deliverables, and acceptance criteria, and drive MVP, pilot, and production delivery.