Enterprise AI Architect MMH261020-5

Role Purpose

Define, govern and evolve the organisation's Enterprise AI Architecture strategy,

standards and roadmap. Provide architecture leadership across Artificial Intelligence,

Machine Learning, Generative AI, agentic AI, intelligent automation, cloud AI platforms

and data ecosystems.

Requirements

• Minimum 8-10 years of experience in Enterprise Architecture, Solution

Architecture, AI/ML Architecture, Data Architecture, Software Engineering or

Platform Architecture.

• Experience with Machine Learning, Generative AI, Intelligent Automation and

Analytics solutions.

• Experience designing cloud-based AI and data architectures across AWS, Azure or

GCP.

• Strong experience with MLOps, LLMOps, model lifecycle management,

monitoring and production support frameworks.

Duties and Responsibilities

KEY ACCOUNTABILITIES/KRAs/KPIs

• Own and maintain the Enterprise AI Architecture Strategy, Roadmap, Standards

and Reference Architectures.

• Provide architecture leadership across GenAI, ML, LLMOps, MLOps, RAG, Vector

Databases, Embeddings, Agentic AI, Copilots and Intelligent Automation

solutions.

• Assess AI use cases based on business value, data readiness, architecture fit,

security, compliance, scalability and cost effectiveness.

• Define architecture guardrails, reusable patterns and implementation standards

for enterprise AI adoption.

• Guide selection and implementation of AI platforms, foundation models, cloud AI

services, vector databases, automation platforms and vendor solutions.

• Define production readiness criteria including evaluation processes, monitoring,

observability, auditability and operational support standards.

• Ensure compliance with Responsible AI, security, privacy, governance and

regulatory requirements.

• Support architecture governance forums and maintain architecture decision

records.

• Review solution designs and provide architecture assurance across AI initiatives.
• Drive enterprise-wide reuse of AI capabilities and minimise fragmented AI

experimentation.

• Mentor architects, engineers and delivery teams on AI architecture best

practices.

• Partner with business, engineering, data, cloud, cyber security, compliance and

procurement teams to deliver scalable AI capabilities.

• Evaluate technology trends and emerging AI innovations to support strategic

planning and continuous improvement.

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