岗位职责/Job Responsibilities:

1. Enterprise Financial AI Architecture Design

• Own the end-to-end AI architecture across data ingestion, LLM inference layer, RAG retrieval augmentation, agent orchestration, API gateways, and front-end interaction—ensuring enterprise-grade scalability, security, and maintainability.
• Lead LLM & RAG architecture decisions: model selection (managed vs. self-hosted), prompt engineering frameworks, hybrid retrieval strategies (dense + sparse vectors), re-ranking, query transformation, context compression, and vector database strategy (e.g., Pinecone, Qdrant, pgvector).
• Design agentic (AI Agent) workflows using frameworks such as LangGraph, LlamaIndex, or Semantic Kernel; architect tool-calling schemas, state management, and human-in-the-loop guardrails for finance-specific tasks—e.g., automated reconciliation, variance analysis, intercompany matching, journal entry validation, and Smart Narrative generation.
• Architect enterprise integration layers: deeply embed AI capabilities into the existing finance technology stack (SAP S/4, BPC, Hyperion, Group Reporting, Blackline, FloQast, Snowflake, Azure Fabric) via APIs, MCP protocols, and event-driven pipelines—ensuring every AI output is traceable to the single-journal-entry level.
• Embed explainability, bias detection, and hallucination guardrails into every AI component; document model assumptions, limitations, and regulatory alignment (SOX, GDPR, China data-export rules, internal audit standards).

2. AI Process Design & End-to-End Delivery

• Re-architect core finance processes as AI-native workflows across the full financial value chain:
  • Accounting & Close: GL automation, intelligent reconciliation, close-task orchestration, flux analysis automation.
  • PTP / OTC: AI-driven AP/AR processing, cash application, vendor risk scoring, credit limit optimization.
  • Consolidation & Reporting: automated intercompany elimination, currency translation intelligence, management report narrative generation.
  • Tax & Treasury: AI-assisted tax provision, transfer-pricing documentation, cash-flow forecasting, liquidity risk alerts.
  • Compliance & Audit: continuous control monitoring, anomaly detection, automated audit evidence collection.
• Own the full delivery lifecycle: from requirements clarification, proof-of-value (PoV), MVP development, pilot deployment, to global rollout—ensuring measurable business value within 90-day cycles.
• Establish production-grade delivery standards: define release management, A/B testing, canary deployment, rollback strategy, and performance monitoring for AI solutions moving from lab to live operations.
• Embed complete data lineage and audit readiness into AI processes: ensure every insight generated by AI is traceable, explainable, and compliant with internal and external audit requirements.

3. Full Financial Value Chain Product Ownership

• Accounting & Close Intelligence: design AI solutions that automate GL reconciliations, accelerate month-end close, and provide real-time close health dashboards with predictive risk flags.
• Consolidation & Group Reporting: architect AI-assisted consolidation workflows—automated intercompany matching, intelligent elimination entries, and narrative commentary that ties directly to GL and sub-ledger data.
• Management Reporting AI: transform redundant report inventories (e.g., 300+ tabular reports) into 3 core narrative-driven pages powered by AI-generated insights, with drill-down capability to single-journal-entry granularity.
• Predictive & Risk Analytics: deliver AI-driven cash-flow forecasting (e.g., XGBoost time-series), LLM-generated management commentary (Smart Narrative), and AI-assisted risk assessment—covering the full pipeline from feature engineering to production visualization.
• Self-Service AI + BI Community: build reusable DAX / SQL / AI Agent templates, train finance super-users across Accounting, Tax, Treasury, and FP&A, and drive fixed-report inventory reduction by 50%.

4. Cross-Functional Technical Leadership

• Lead a cross-functional delivery squad (AI engineers, data engineers, accounting SMEs, tax/treasury experts, compliance, regional controllers) across US, EMEA, and APAC; drive architecture decisions, conduct technical reviews, and resolve complex engineering blockers.
• Establish AI architecture review mechanisms, coding standards, prompt management protocols, and agent evaluation frameworks (e.g., Ragas, TruLens) to ensure consistent delivery quality.
• Mentor and upskill the team on modern AI engineering practices: prompt engineering, RAG optimization, agent evaluation, MLOps for LLMs, and finance-domain data modeling.

5. Executive Engagement & Value Realization

• Engage directly with Group / Regional CFO, CAO, Corporate Controller, and Finance Directors; translate complex AI architecture into business impact language, and use data storytelling to demonstrate decision-efficiency gains.
• Quantify ROI upfront for every AI initiative: document business assumptions, cost-benefit analysis, and success metrics before development begins.
• Drive adoption through embedded partnership: ensure AI becomes the default workflow layer across Accounting, Consolidation, Tax, Treasury, and FP&A—not a side tool.

岗位要求/Job Requirements：

• Accounting & Finance Depth:
  • 8+ years of hands-on experience in Finance Transformation, Accounting Operations, Finance BI, or finance-system implementation.
  • At least 3 years of direct exposure to Group / Regional CFO or Corporate Controller level.
  • Solid grasp of the full financial value chain: GL operations, month-end / quarter-end close, intercompany accounting, consolidation (US GAAP / IFRS), management reporting, tax, treasury, and internal controls.
  • Proven track record of designing and delivering finance solutions that touch both operational accounting and strategic reporting.
• AI Architecture & Engineering:
  • 6+ years of software engineering or solution architecture experience, with proven technical leadership delivering production-grade LLM / Gen-AI / Agent / RAG solutions (POC-only experience is insufficient).
  • Deep understanding of LLM capabilities and boundaries, RAG architecture patterns, prompt engineering, tool calling (Function Calling), agent orchestration, and model evaluation.
  • Hands-on experience designing scalable, secure, and maintainable enterprise AI architectures covering APIs, data ingestion, vector databases, IAM, observability, and cloud-native deployment.
  • Proficiency in Python, REST APIs, asynchronous processing, distributed systems, and modern backend development.
• Finance Technology Stack: Deep knowledge of SAP FICO, BPC, Group Reporting, Blackline, or FloQast data models; fluent in SQL, Power Query, DAX, and data-modeling best practices.
• End-to-End Delivery Record: Two or more end-to-end deployments of global management-reporting, accounting automation, or AI platforms (Power BI, Tableau, SAC, Anaplan, or equivalent) in Fortune 500 environments.
• Bilingual Communication: Professional-level English for technical architecture reviews, documentation, and cross-regional stakeholder influence; proficiency in Mandarin is a strong plus.
• AI in Finance Practice: Demonstrated experience applying AI/ML/Gen-AI across the financial value chain—e.g., automated reconciliation, XGBoost cash prediction, LLM-generated Smart Narrative, intercompany anomaly detection, AI-driven risk assessment—covering the full cycle from data pipeline, feature engineering, model development to production visualization, with documented business assumptions.
• Compliance & Security: Working knowledge of enterprise data privacy, security, and responsible-AI requirements; familiarity with SOX controls, GDPR, and China data-export regulations as they apply to AI-generated financial data.

Nice-to-Have Attributes

• Professional Certifications: CPA, CMA, ACCA, PMP, Scrum Product Owner, TOGAF, Lean Six Sigma Black Belt, or cloud/AI architecture certifications (e.g., Azure Solutions Architect, Azure AI Engineer).
• Cloud & Data Platforms: Hands-on experience with Snowflake, Azure Fabric, Azure OpenAI, Databricks, BW/4HANA supporting large-scale financial data processing.
• AI Engineering Infrastructure: Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, Kubernetes, Docker, CI/CD, infrastructure-as-code, and production AI observability/evaluation platforms.
• Industry Background: Manufacturing, retail, or high-tech supply chain; familiarity with standard costing, transfer pricing, plant-level P&L, and inventory accounting.
• Thought Leadership: Published articles or conference speaking experience on finance digitalization, AI in accounting, or enterprise AI architecture.