Shape the Future with Dun & Bradstreet At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.

Responsibilties and Requirements

About the Role

At Dun & Bradstreet, data is not just an asset—it is the context layer that powers intelligent decision-making. As our AI Solution Architect in Hong Kong, you will be a Forward Deployed Engineer who sit at the intersection of D&B’s global data network, cutting-edge AI infrastructure, and the real-world systems of largest enterprises.

This is not a back-office engineering role. You and your team will be embedded with clients—from multinational banks and Fortune 500 manufacturers to cross-border trade platforms—to architect, deploy, and operationalize AI solutions that integrate D&B’s proprietary commercial data directly into their ERP, CRM, SCM, and risk management systems.

You will operate in one of the world’s most regulated financial centers, solving high-stakes problems where data privacy, cross-border compliance, and real-time business intelligence converge.

 

What You’ll Do

Own End-to-End AI Deployment

•  Scope, architect, and deliver production-grade AI solutions (RAG pipelines, Agent workflows, predictive analytics) using D&B data assets and client infrastructure.

•  Operate in 6–12 week timeboxes, moving from ambiguous business requirements to working prototypes that demonstrate measurable ROI.

Be the Technical-Strategic Bridge

•  Serve as the senior technical counterpart to C-suite, VP, and Head of Data stakeholders at client organizations.

•  Translate complex engineering trade-offs into CFO- and CIO-ready business cases—covering revenue impact, cost reduction, and risk mitigation.

Ensure Enterprise-Grade Compliance

•  Guarantee all deployments meet SOC 2, ISO 27001, and regional regulatory standards including China PIPL, Hong Kong PDPO, and financial services sector requirements.

•  Navigate cross-border data governance with rigor—D&B’s credibility depends on it.

Drive Product-Market Feedback Loops

•  Channel live client insights back to D&B’s Product and AI Labs teams to refine our data APIs, model performance, and vertical-specific solutions.

 

Who You Are

Engineering Depth

•  7+ years of product-grade software engineering experience (Python, SQL, Java/Go preferred).

•  Proven expertise in cloud infrastructure (AWS/Azure/GCP), data pipelines (Airflow, Spark), and enterprise system integration (SAP, Salesforce, Snowflake, etc.).

•  Hands-on experience with the modern GenAI stack: LLMs, vector databases, retrieval architecture, fine-tuning (LoRA), and Agent frameworks (LangChain, LangGraph, DSPy).

Client-Facing Leadership

•  3+ years in consulting, solutions engineering, or client-embedded technical roles.

•  A track record of delivering AI/ML projects in production within regulated industries (financial services, credit risk, supply chain, or trade finance highly preferred).

•  Demonstrated ability to manage stakeholder complexity and navigate organizational politics to ship outcomes—what we call High Agency.

Strategic & Commercial Acumen

•  Comfortable discussing P&L impact, data monetization strategy, and competitive positioning with senior executives.

•  Experience building and scaling technical teams in a high-growth or transformation environment.

Language & Compliance

•  Business fluency in English and Mandarin (written and spoken). Cantonese is a strong plus.

•  Working knowledge of data privacy, security frameworks (SOC 2, FedRAMP, HIPAA), and APAC regulatory landscapes.

Nice-to-Have

•  Prior experience at top-tier consulting firms, or AI-native enterprises with embedded deployment models.

•  Deep domain expertise in commercial credit data, supply chain risk, KYB/KYC workflows, or trade intelligence.

•  Published work, conference speaking, or open-source contributions in applied AI or data engineering.

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