AI Architect

The AI Architect defines and owns the end-to-end technical vision for AI solutions within the HKBN Enterprise Solutions portfolio, with a primary focus on the HKBN AI+ platform. This role translates enterprise business problems into robust, scalable AI architectures — covering model selection, RAG pipelines, Agent-as-a-Service frameworks, token-based inference infrastructure, security, and operationalisation — while ensuring alignment with HKBN's technology standards, cost efficiency, and delivery timelines. The AI Architect will play a pivotal role in shaping how HKBN AI+ is positioned, evolved, and delivered to enterprise clients across Hong Kong.

About the role:

AI Architecture & Platform Design

●        Design end-to-end AI solution architectures on HKBN AI+, covering data and knowledge management, data ingestion, model fine-tuning, RAG pipelines, agentic workflows, inference serving, monitoring, and lifecycle management.

●        Evaluate and recommend model strategies based on enterprise use case requirements, latency, cost, and accuracy trade-offs.

Technical Leadership

●        Establish AI architecture principles, design patterns, and governance frameworks.

●        Lead technical spikes and proof-of-concept initiatives to validate architectural approaches on HKBN AI+.

Stakeholder & Pre-Sales Engagement

●        Present architectural options, trade-offs, and platform capabilities to enterprise client executives and technical stakeholders.

●        Support PreSales teams with AI architecture input, effort estimation, infrastructure sizing, and technical validation for multi-tower deals.

About you:

●        Bachelor's degree in Computer Science, AI, Data Science, Engineering, or a related field. Master's or PhD preferred but not required.

●        5+ years in software or solutions architecture, with at least 2 years focused on AI/ML architecture.

●        Experience in regulated industries (finance, healthcare) or Hong Kong enterprise market is preferred.

●        Deep understanding of LLM architectures, RAG patterns, vector databases (e.g. Milvus, Pinecone, Qdrant), and prompt engineering.

●        Hands-on experience with AI/ML platforms and inference serving (e.g. vLLM, TGI, OpenAI-compatible APIs).

●        Experience designing production AI systems — covering inference serving, monitoring, guardrails, and cost management.

●        Familiarity with Agent-as-a-Service and Token-as-a-Service commercial models for enterprise AI.

●        Experience working with private, hybrid, and public cloud infrastructure (Alibaba Cloud, AWS, Azure) and container orchestration (Kubernetes).

●        Experience with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen).