AI Solution Architect (R-19393)

2 months ago

Shenzhen, ChinaSenior

Responsibilities

Scope, architect, and deliver production-grade AI solutions including RAG pipelines, agent workflows, and predictive analytics.
Move from ambiguous business requirements to working prototypes with measurable ROI in 6–12 week timeboxes.
Integrate Dun & Bradstreet data into client ERP, CRM, supply chain, and risk-management systems.
Serve as the senior technical counterpart to C-suite, VP, and Head of Data stakeholders.
Translate technical trade-offs into business cases covering revenue impact, cost reduction, and risk mitigation.
Ensure deployments meet SOC 2, ISO 27001, regional privacy regulations, and financial-services requirements.
Navigate cross-border data governance and regulated-industry compliance.
Share client insights with Product and AI Labs teams to improve APIs, model performance, and vertical solutions.

7+ years of product-grade software engineering experience with Python and SQL; Java or Go preferred.
Proven expertise with AWS, Azure, or GCP; Airflow or Spark; and enterprise integrations such as SAP, Salesforce, or Snowflake.
Hands-on experience with LLMs, vector databases, retrieval architecture, LoRA fine-tuning, and agent frameworks including LangChain, LangGraph, or DSPy.
3+ years of experience in consulting, solutions engineering, or client-embedded technical roles.
Experience delivering AI/ML projects in production within regulated industries; financial services, credit risk, supply chain, or trade finance experience is preferred.
Experience managing complex stakeholders and shipping outcomes in client environments.
Experience discussing P&L impact, data monetization strategy, and competitive positioning with senior executives.
Experience building and scaling technical teams in high-growth or transformation environments.
Business fluency in written and spoken English and Mandarin is required; Cantonese is a strong plus.
Working knowledge of data privacy, SOC 2, FedRAMP, HIPAA, and APAC regulatory landscapes.
Prior top-tier consulting or AI-native enterprise experience, commercial credit data or supply-chain risk expertise, and published or open-source applied AI or data engineering work are nice to have.

5,001-10,000 employees