Manager / Associate Director AI Architect

About the Opportunity

As an AI Architect (Grade E), you will be responsible for designing and governing AI, Machine Learning, Generative AI, and Agentic AI solutions across the organisation. You will work closely with Product Owners, Engineers, Data Scientists, Data Engineers, Security teams, and Enterprise Architects to create architectures that deliver measurable business value while complying with technology, risk, and governance standards.

You will provide technical leadership across complex initiatives, define architecture patterns, and ensure AI products are designed for scalability, security, resilience, and operational excellence. AI architecture is a core component of future technology strategy and plays a critical role in accelerating innovation across the Group.

Key Responsibilities

AI Solution Architecture

• Design end-to-end AI, ML, GenAI, and Agentic AI architectures aligned to business outcomes.
• Define solution blueprints covering data ingestion, model lifecycle, orchestration, deployment, monitoring, and governance.
• Create architecture patterns and reusable frameworks that accelerate AI delivery.
• Evaluate and select appropriate AI technologies, platforms, and tooling.

Technical Leadership

• Provide architecture leadership across multiple initiatives and delivery teams.
• Guide engineering teams on architectural best practices and AI adoption.
• Support architectural reviews, design governance, and technology decisions.
• Collaborate with Enterprise Architecture and platform teams to align with strategic roadmaps.

Generative AI & Agentic AI

• Design architectures using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, orchestration frameworks, and agent-based systems.
• Define patterns for prompt management, evaluation, observability, and AI safety controls.
• Support the adoption of secure and governed GenAI capabilities across the organisation.

Governance, Risk & Security

• Ensure AI solutions comply with Responsible AI, security, privacy, and risk requirements.
• Contribute to model governance, architecture standards, and technical assurance processes.
• Partner with Cyber Security, Risk, and Compliance teams to design secure AI capabilities.
• Embed monitoring, auditability, explainability, and operational controls into AI solutions.

Stakeholder Engagement

• Translate complex technical concepts into business-friendly language.
• Influence senior technical and business stakeholders.
• Facilitate architecture workshops and solution design sessions.
• Build strong relationships across product, engineering, architecture, and business teams.

Essential Skills & Experience

• Strong experience designing enterprise-scale AI and Machine Learning solutions.
• Experience with Generative AI, LLMs, Agentic AI frameworks, and RAG architectures.
• Expertise in cloud-native architecture, preferably Google Cloud Platform (GCP).
• Knowledge of MLOps, LLMOps, model deployment, monitoring, and lifecycle management.
• Experience with distributed systems, APIs, microservices, and event-driven architectures.
• Strong architecture modelling, design documentation, and governance experience.
• Understanding of Responsible AI, AI security, and regulatory considerations.
• Excellent stakeholder management and communication skills.

Preferred Technical Skills

• Google Cloud Platform (Vertex AI, BigQuery, GCS).
• Python and AI/ML ecosystems.
• LangGraph, LangChain, ADK, orchestration frameworks.
• Vector databases and Retrieval-Augmented Generation (RAG).
• Kubernetes, Docker, CI/CD, DevOps practices.
• Data architecture and data engineering concepts.
• Enterprise integration and API management.

Leadership Expectations

• Act as a recognised technical leader within the AI architecture community.
• Mentor Solution Architects, Engineers, and Data Scientists.
• Drive adoption of architecture standards and best practices.
• Promote innovation while maintaining strong governance and risk management.
• Influence strategic technology decisions through thought leadership.

Success Measures

• Delivery of scalable and secure AI solutions.
• Adoption of reusable architecture patterns.
• Improved time-to-market for AI products.
• Compliance with architecture, risk, and governance standards.
• Positive business outcomes through AI-enabled capabilities.