Lead AI Platform Architect

Lead AI Platform Architect

About the opportunity

An established enterprise is expanding its AI capabilities and is seeking a Lead AI Platform Architect to define the architecture, technology strategy and roadmap for its enterprise AI platform.

You will shape the shared capabilities that enable teams across the organisation to develop, deploy and operate secure, scalable and reusable AI solutions. The role combines strategic architecture ownership, emerging technology evaluation and technical leadership across foundation models, agentic AI, multimodal capabilities and model lifecycle management.

Working closely with Enterprise Architecture, Platform Engineering, Operations, Cybersecurity and business stakeholders, you will establish the standards and architectural direction supporting enterprise-wide AI adoption.

Key responsibilities

• Own the enterprise AI platform architecture, reference architecture and technology roadmap, aligning platform development with business priorities and enterprise requirements.
• Define reusable platform capabilities, design patterns, technical frameworks and architectural standards that support consistent AI delivery across teams.
• Evaluate and benchmark hosted, self-hosted and open-weight foundation models, establishing model selection strategies based on capability, performance, cost and governance requirements.
• Define platform architecture for multimodal AI, covering language, speech, voice, documents, images, vision-language and video.
• Establish architectural approaches and best practices for model optimisation.
• Define model lifecycle standards covering evaluation, fine-tuning, deployment, versioning, retraining, monitoring, regression management and retirement.
• Lead technology assessments, proofs of concept and pilots to validate feasibility, scalability, business value and enterprise readiness.
• Recommend build, buy or partner approaches, considering architecture alignment, total cost of ownership and long-term platform requirements.
• Engage technology partners, cloud providers and AI vendors to assess emerging capabilities and inform the platform roadmap.
• Review architecture proposals for platform enhancements, new technologies and architectural exceptions.
• Present and defend architecture recommendations to senior stakeholders and relevant architecture review boards.
• Embed AI security, Responsible AI, privacy, data governance and regulatory requirements into platform design.
• Guide engineering and operations teams on implementation, observability, reliability, resilience, performance and lifecycle management.
• Partner with AI Solution Architects to ensure shared platform capabilities support evolving application requirements.
• Mentor architects and senior engineers, strengthening technical decision-making and architecture practices.

Experience and capabilities

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems or a related technical discipline.
• At least 10 years of experience in enterprise, platform, software or cloud architecture, including the design of large-scale enterprise platforms.
• At least 3 years of experience designing enterprise AI, machine learning or generative AI platforms.
• Demonstrated ownership of platform architecture, technology roadmaps, architecture standards and reusable capabilities.
• Experience presenting architecture proposals to senior management and architecture review boards.
• Strong foundations in distributed systems, microservices, event-driven architecture and API-first design.
• Strong understanding of cloud-native, hybrid cloud and on-premises architectures, including container orchestration.
• Strong knowledge of enterprise AI platforms, AI engineering ecosystems, Model-as-a-Service and agentic AI platforms.
• Understanding of foundation models, LLMs, small language models, multimodal AI and enterprise deployment approaches.
• Experience defining model lifecycle architecture, including evaluation, fine-tuning, deployment, monitoring and governance.
• Familiarity with RAG, AI agents, workflow orchestration, Model Context Protocol, API integration and enterprise integration patterns.
• Experience incorporating security, Responsible AI, privacy and governance requirements into architecture standards.
• Understanding of AI platform scalability, reliability, observability, resilience and performance optimisation.
• Strong stakeholder management, communication, documentation and mentoring skills.

Interested candidates please reach out to [Confidential Information] with your updated CV. Only shortlisted candidates will be notified.