AI Data Architecture

Randstad Hong Kong; Hong Kong; On-site; HKD 1,200,000 - 1,800,000; Full time

Job summary

Randstad Hong Kong partners with a premier multinational financial institution in Hong Kong to recruit a Senior AI & Data Architect to lead enterprise-scale GenAI innovation across Asia. You will build scalable data ecosystems, establish roadmaps, governance, and Agentic AI stacks, and drive end-to-end architecture for data ingestion, feature stores, real-time processing, and model orchestration.

The role requires 10+ years of data architecture experience, 4+ years deploying AI/ML

Qualifications

• Bachelor's degree or above in a technical field.
• 10+ years of data architecture experience with leadership.
• 4+ years deploying enterprise AI/ML and GenAI infra.
• LLM stack exposure: RAG, Agentic AI, governance, MCP.
• Deep Azure, data lakes, Databricks, NoSQL, Azure SQL/Oracle.
• Python or R for ML pipelines; Power BI/Tableau for analytics.
• Strong cross-functional influence across enterprise teams.

Responsibilities

• Establish roadmaps, blueprints, and governance for AI/ML and GenAI stacks.
• Lead end-to-end architecture for data ingestion, feature stores, real-time prep, and model orchestration.
• Translate business goals into technical specs; align with EA and IT Delivery.
• Resolve bottlenecks in deployment, scalability, and cloud integrations.
• Enforce ethical AI, data security, and HKMA/IA compliance.

Skills

Leadership

Cross-functional influence

Strategic thinking

Education

Bachelor's degree in Computer Science

Tools

Azure

Databricks

Power BI

Tableau

NoSQL databases

Azure SQL

Oracle

Python

R

Job description

Are you a Senior AI & Data Architect looking to lead enterprise-scale GenAI innovation across Asia? A premier multinational financial institution in Hong Kong is seeking a technical leader to build scalable data ecosystems, deploy Agentic AI, and steer HKMA-compliant AI frameworks within their centralised Center of Excellence.

about the company.

Our client is a premier multinational financial institution with a century-long legacy in Asia. Undergoing a major digital transformation, they are building next-generation digital ecosystems across life, health, and wellness solutions to deliver seamless, technology-driven experiences across the region.

about the team.

You will join a high-performing, centralised Innovation & Data Science Center of Excellence (CoE). The team brings together cross-functional experts in artificial intelligence, machine learning, and cloud technology who are dedicated to pioneering scalable, high-performance enterprise data architectures for advanced digital platforms.

about the job.

Establish structural roadmaps, foundational blueprints, and governance frameworks to support enterprise AI/ML and Agentic AI technology stacks.

Lead end-to-end architecture design for data ingestion, feature store preparation, real‑time preprocessing, and large-scale model orchestration under strict SLAs.

Translate strategic business goals into technical architecture specifications while aligning domain roadmaps with Enterprise Architecture and IT Delivery teams.

Resolve complex bottlenecks in ML model deployments, pipeline scalability, distributed data processing, and cloud platform integrations.

Enforce ethical AI practices, robust data security, and local regulatory compliance (including HKMA and IA standards) across all AI workflows.

skills & experience required.

• Bachelor's degree or above in Computer Science, Software Engineering, Data Engineering, or a related technical discipline.
• 10+ years of professional data architecture experience, ideally within insurance, wealth management, or healthcare sectors, with proven leadership abilities.
• 4+ years of direct experience designing and deploying large-scale enterprise AI/ML and GenAI infrastructure.
• Hands-on exposure to the LLM stack, including RAG architectures, Agentic AI, prompt engineering workflows, model governance, and MCP protocols.
• Deep technical experience with Azure cloud environments, modern data lakes, Databricks, NoSQL databases, and relational platforms (Azure SQL, Oracle).
• Proficiency in Python or R for ML pipelines, paired with enterprise analytics platforms such as Power BI or Tableau.
• Strong cross-functional influence to steer engineering and business teams through fast-moving enterprise environments.