About the Role

We are seeking a passionate and technically strong Head, AI Platform (Principal AI Architect) to join Kuok Group's AI Centre of Excellence. This person will own the architecture, engineering standards, and technical infrastructure that every AI use case across the Group runs on — from the LLM gateway and RAG data foundation through to CI/CD pipelines, model observability, and governance controls.

The successful candidate will lead the AI Platform cluster directly, set technical standards for the Applied AI cluster, and serve as the engineering authority whose review and approval gates every production deployment — operating as architect, reviewer, and standards-setter to enable the rest of the team to build at speed without compromising on quality or security.

This is a platform-first role, focused on building the durable technical foundation that the Group's AI capability compounds on over time.

Key Responsibilities

Architecture & Technical Standards

Design and own the end-to-end AI platform architecture — LLM gateway, RAG layer, vector + knowledge graph foundation, model serving, observability, and eval infrastructure
Set and enforce engineering standards across both the AI Platform and Applied AI clusters: code review criteria, security baselines, deployment gates, and documentation requirements
Own the technical roadmap for the Group's broader AI infrastructure stack

Platform Cluster Leadership

Manage the AI Platform team cluster directly: AI Platform Engineer, AI Data Architect, and LLM Ops / MLOps Engineer
Provide technical direction and functional oversight to the AI Data Architect on AI data foundation design — embedding strategy, vector DB design, knowledge graph ingestion approach
Coordinate with the Applied AI cluster on technical requirements for use case delivery and review Applied AI Engineers architecturally significant decisions
Own offshore and vendor technical oversight: standing weekly tech review, templated acceptance criteria, and code review consistency for all externally delivered work

Platform Build & Infrastructure

Partner with the AI Platform Engineer on CI/CD pipeline design, environment provisioning, infra-as-code, and the deployment platform (staging, production, rollback)
Oversee the model serving and gateway layer including API access, rate limits, and model switching controls
Ensure LLM observability (LangSmith and similar), cost telemetry, and token controls are in place and monitored as a first-class concern
Establish and maintain the eval pipeline infrastructure in partnership with the LLM Ops / MLOps Engineer; make evals a gated handover criterion for every production use case

AI Data Foundation

Work with the AI Data Architect to design the hybrid vector + knowledge graph layer that underpins RAG across all BUs — the enterprise semantic memory
Set the strategic direction for embedding pipelines, chunking strategies, and vector DB design, with the AI Data Architect owning day-to-day execution

Governance, Security & Risk

Partner with the AI Governance & Compliance Lead on security architecture, red team drill cadence, and data privacy protocols across all deployed agents and workflows
Provide technical inputs to AI Governance & Compliance Lead

Stakeholder Engagement

Represent the technical platform view in Steerco updates, architecture reviews, and vendor selection decisions
Translate platform architecture constraints and investments into business-legible language for non-technical senior stakeholders
Evaluate and negotiate with vendors on technology stack decisions — model providers, observability tooling, infra providers

Requirements

Must-Have

Proven architecture experience in AI / ML systems — you have designed and shipped production systems that include LLM-based components, not just traditional ML pipelines
Strong engineering fundamentals: proficiency in Python, cloud infrastructure (Azure preferred), and DevOps practices including CI/CD, IaC, and environment management
Hands-on experience with LLM infrastructure: model gateway design, prompt versioning, observability tooling (LangSmith or equivalent), and cost controls
Demonstrated experience with RAG architecture — chunking strategies, vector DB design, retrieval optimisation, re-ranking, and knowledge graphs
Agentic AI framework knowledge: LangGraph, CrewAI, or equivalent — and an understanding of the operational differences between stateless and stateful agent patterns
Engineering leadership track record: you have set technical standards, run code reviews, and led a team to deliver to a high bar
Strong communication skills — able to write clearly, produce architecture decision records, and present to technical and non-technical audiences

Strong Advantage

Experience with knowledge graph design and ontology modelling (Neo4j or equivalent)
MLOps or LLMOps background: model versioning, A/B testing for model outputs, eval pipeline design, LLM-as-judge patterns
Security architecture experience relevant to AI systems: threat modelling for agentic workflows, data privacy design, red teaming
Vendor management experience — MSA negotiation, offshore technical oversight, acceptance criteria design
Familiarity with enterprise conglomerate environments: multiple BUs, federated data, diverse system landscapes