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).