Lead AI Platform Architect #AIDA

Lead AI Platform Architect

The Lead AI Platform Architect defines the architecture, technology strategy and roadmap for our client’s enterprise AI platform, enabling secure, scalable and reusable AI capabilities across the organisation. Working at the centre of the AI platform ecosystem, the role supports consistent AI solution delivery while ensuring alignment with business priorities, technology advancements and enterprise governance requirements.

Role Overview

The role provides technical authority across AI platform strategy, foundation models, agentic AI, model lifecycle management, AI engineering frameworks and platform capabilities. It works closely with Enterprise Architecture, AI Platform Engineering, AI Platform Operations, Cybersecurity, AI Solution Architects and business stakeholders. Success means continuously evolving an interoperable, reliable and enterprise-ready AI platform while providing technical direction to architects and engineers.

Key Skills & Experience

• Strong foundation in enterprise platform architecture, distributed systems and cloud-native technologies
• At least 10 years of experience in enterprise architecture, platform architecture, software architecture or cloud architecture, designing large-scale enterprise platforms
• At least 3 years of experience designing AI, Machine Learning or Generative AI platforms in an enterprise environment
• Proven experience defining platform architecture, technology roadmaps, architecture standards and reusable platform capabilities
• Experience leading architecture governance, including presenting architecture proposals to senior management and Architecture Review Boards
• Experience working across Enterprise Architecture, Engineering, Infrastructure, Cybersecurity, Data and Business teams
• Strong knowledge of enterprise AI platform architecture, including AI application platforms, AI engineering ecosystems, Model-as-a-Service and agentic AI platforms
• Strong understanding of foundation models, Large Language Models, Small Language Models, multimodal AI and enterprise AI deployment strategies
• Experience defining AI model lifecycle architecture, including model evaluation, fine-tuning strategies, deployment, versioning, monitoring and governance
• Strong understanding of cloud-native, hybrid cloud and on-premises platform architectures, distributed computing and container orchestration principles
• Good understanding of enterprise AI application patterns, including Retrieval-Augmented Generation, AI agents, workflow orchestration, Model Context Protocol, API integration and enterprise integration architectures
• Strong knowledge of enterprise architecture principles, distributed systems, microservices, event-driven architectures and API-first design
• Experience defining architecture standards for AI security, Responsible AI, data governance, model governance, privacy and regulatory compliance
• Good understanding of AI platform reliability, scalability, observability, resiliency and performance optimisation principles
• Strong ability to evaluate emerging AI technologies and translate technology trends into enterprise platform capabilities, architecture standards and technology roadmaps
• Strong strategic, analytical and systems thinking, with the ability to simplify complex architecture and technology decisions for diverse stakeholders
• Excellent collaboration and stakeholder management skills across Enterprise Architecture, AI Solution Architecture, Platform Engineering, Platform Operations, Cybersecurity and Business teams
• Strong communication, presentation and documentation skills, with experience presenting architecture proposals and influencing senior management and governance boards
• Proven technical leadership and mentoring skills, with the ability to guide architects and engineers, influence platform direction and drive architecture best practices
• Strong innovation mindset and continuous learning, with a passion for exploring emerging AI technologies and translating industry trends into enterprise platform capabilities

Key Responsibilities

• Define and maintain the enterprise AI platform architecture, technology roadmap and reference architecture for the central AI platform
• Establish reusable AI platform capabilities, architectural principles, design standards and technical frameworks
• Evaluate, benchmark and recommend hosted, self-hosted and open-weight foundation models against business, technical and governance requirements
• Engage with technology partners, cloud providers, AI vendors and the wider technology ecosystem to assess emerging platform capabilities and products
• Lead proof-of-concept, technology evaluation and pilot initiatives for emerging AI technologies, frameworks, platforms and commercial solutions
• Establish platform architecture and best practices for model optimisation techniques
• Define the enterprise architecture for language, speech, voice, document, image, vision-language and video models
• Define enterprise standards and governance for AI model lifecycle management, including model versioning, retraining strategies, regression management, performance evaluation and model retirement
• Assess emerging AI technologies and recommend Build, Buy or Partner strategies based on business value, enterprise architecture alignment, scalability and total cost of ownership
• Publish and maintain AI platform reference architectures, reusable design patterns, technical standards, implementation guidelines and best practices
• Collaborate with Enterprise Architecture, Cybersecurity and governance teams to align platform architecture with enterprise architecture principles, security requirements and regulatory standards
• Review and govern architecture proposals for new AI platform capabilities, technology adoption and platform enhancements
• Present and defend AI platform architecture proposals to the Solution Review Board, senior management and Group/Enterprise Architecture
• Seek Architecture Review Board clearance where required for new platform capabilities, architecture patterns, technology adoption, technology exceptions or enterprise architectural changes
• Provide architectural direction and technical guidance to AI Platform Engineering teams during platform capability planning and implementation
• Define platform observability, operational architecture, reliability requirements and lifecycle management strategies for production AI services with AI Platform Engineering and Operations
• Partner with AI Solution Architects to ensure reusable platform capabilities support scalable AI solution delivery
• Work with AI Security Architects to embed AI security, Responsible AI, privacy and governance requirements into platform architecture and design
• Mentor Platform Architects and senior technical engineers to promote architecture excellence, innovation and continuous improvement
• Advise senior management, Group/Enterprise Architecture, technology teams and business stakeholders on enterprise AI platform strategy

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Information Systems or a related technical discipline
• On-site working arrangement
• At least 10 years of experience in enterprise architecture, platform architecture, software architecture or cloud architecture
• At least 3 years of experience designing AI, Machine Learning or Generative AI platforms in an enterprise environment

If you have the relevant skills and experience, please apply with an updated CV.