AI Solutions Architect

The Role

As AI Solutions Architect, you will partner to define the technical architecture, standards, and reusable solution patterns that enable our businesses to adopt and operationalize AI. This is a senior technical leadership role focused on enterprise AI architecture rather than traditional software development.

Working alongside Business CEOs, Product Leaders, engineering teams, AI Champions, and AI Pods, you will design scalable AI solutions that accelerate product innovation, automate business processes, and improve customer outcomes while keeping them secure, maintainable, and aligned with enterprise architecture principles. You will support AI Work Pods with architectural guidance, technical coaching, and reusable frameworks, while each business retains ownership of its own implementation.

Key Relationships

• Head of Artificial Intelligence: Reporting to and escalation.

• AI Transformation Lead: Matrix to align technical architecture with transformation delivery.

• Business CEOs, Product Leaders, AI Champions, and AI Pods across participating businesses.

• Product Experts, engineering teams, and Security, Infrastructure, and Engineering functions, on delivery, enterprise standards, and Responsible AI.

Key Responsibilities

In this role you will:

• Define and maintain best practices for the Portfolio AI Reference Architecture, its guiding principles, and reusable, secure solution patterns that align with existing software platforms and technology strategies.

• Evaluate and recommend enterprise AI platforms/technologies and build reference implementations using leading AI tools.

• Set enterprise standards and engineering best practices across the AI stack, spanning model evaluation, testing and validation, observability, performance monitoring, and version management.

• Embed AI coding agents, automated code review, and testing automation across the software development lifecycle, in line with the AI-First Engineering playbook.

• Partner with AI Pods to translate business problems into practical, commercially viable solution architectures, and lead Product Experts and engineering teams through solution discovery, delivery, and implementation.

• Embed security, privacy, auditability, governance, and Responsible AI principles into every solution, supporting compliance with regulatory and customer requirements.

• Mentor engineering teams, AI Champions, and AI Pods, and deliver technical workshops and architecture reviews.

• Build reusable assets for the AI Center of Excellence such as prompt libraries, reference architectures, templates, and playbooks to allow the growth of a portfolio-wide community of technical practice.

• Evaluate emerging AI technologies, run proof-of-concept initiatives, and support the Head of AI in shaping the portfolio AI technology roadmap and investment priorities, balancing innovation with architectural discipline. Where technical standards and commercial delivery timelines conflict, escalate to the Head of AI as the final decision-maker.

Success in the First 12 Months

• Portfolio AI Reference Architecture established and adopted.

• Reusable AI components developed and adopted across multiple businesses, reducing duplicated development.

• A reference RAG or agentic pattern documented and piloted in production by at least one AI Pod within 90 days.

Skills & Experience

We’re looking for a senior technical leader who brings:

• Experience in enterprise software architecture, solution architecture, software engineering, or cloud platform leadership.

• Hands-on experience delivering AI, machine learning, or intelligent automation solutions.

• Experience embedding AI coding agents and automated testing/code-review tools into engineering workflows.

• A strong background designing enterprise, cloud-native, and API-first architectures and integrating AI into SaaS products, plus a track record developing reusable frameworks and defining architecture standards and governance.

• The judgment to balance innovation with operational stability, and the influence to shape architectural decisions across multiple businesses.

• Strong technical leadership, mentoring, and systems-thinking skills, with the ability to communicate complex concepts to non-technical stakeholders.

• Pragmatic, collaborative, and detail-oriented, with a strong engineering mindset and a bias toward practical delivery.

• Curious about emerging AI and continuously learning, calm under pressure, and committed to responsible AI.

Technical Expertise

Must-have

• Generative AI

• Large Language Models (LLMs)

• Agentic AI

• Retrieval-Augmented Generation (RAG)

• AI orchestration frameworks

• Prompt engineering

• Model evaluation

• AI observability

• AI security

• Vector databases

Nice-to-have

• Enterprise integration patterns

• Cloud platforms (Azure, AWS, and Google Cloud)

• Microsoft Copilot ecosystem

• OpenAI and Anthropic APIs

Experience with technologies such as Semantic Kernel, LangChain, AutoGen, Model Context Protocol (MCP), or similar AI orchestration frameworks will be highly regarded.