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AI Architect - Generative AI & Agentic AI Key Responsibilities Define and lead the architecture, design and implementation strategy for enterprise-scale Artificial Intelligence and Generative AI solutions. Establish architectural patterns and technical standards for the adoption of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and intelligent automation across business applications. Design scalable AI architectures incorporating foundation models, embedding technologies, vector databases, knowledge retrieval, orchestration frameworks and enterprise integration services. Define strategies for integrating AI capabilities into existing enterprise technology landscapes, ensuring interoperability, security, scalability and alignment with wider technology architecture principles. Lead the design of Agentic AI solutions, including autonomous agents, multi-agent collaboration, reasoning workflows, tool integration and intelligent process orchestration. Evaluate and recommend appropriate AI models, platforms, frameworks and infrastructure based on business requirements, technical suitability, performance, cost and long-term maintainability. Define architectural approaches for RAG pipelines, knowledge management, semantic search, contextual retrieval and enterprise data integration. Establish standards and architectural guidelines for prompt engineering, model selection, fine-tuning, model evaluation and AI solution optimisation. Drive the development of responsible AI architectures, incorporating governance, security, data privacy, explain ability, bias mitigation and appropriate safety controls. Define deployment and operational strategies for AI solutions, including cloud architecture, containerisation, scalability, monitoring, resilience and ongoing model life cycle management. Provide technical leadership throughout the solution life cycle, from architecture and proof of concept through to engineering delivery and production deployment. Work closely with business stakeholders, enterprise architects, data teams and engineering functions to translate strategic business objectives into robust AI architecture solutions. Provide architectural oversight, technical direction and guidance to engineering teams, ensuring implementation aligns with agreed architectural principles and design standards. Stay informed of emerging AI technologies, frameworks and architectural approaches, identifying opportunities to improve enterprise capabilities and accelerate AI adoption. Essential Qualifications and Skills Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology or a related technical discipline. 12+ years of experience across software engineering, solution architecture or technical design, with a strong background in enterprise application development. Minimum 2 years of hands-on experience designing and delivering Generative AI and LLM-based solutions. Strong understanding of modern AI architectures, including foundation models, transformer architectures, embeddings, RAG, semantic search and vector database technologies. Extensive experience designing Agentic AI architectures, including autonomous agents, multi-agent orchestration, reasoning-based workflows and intelligent tool integration. Experience with AI orchestration and development frameworks, alongside a strong understanding of their architectural capabilities and limitations. Strong understanding of LLM integration patterns, model selection, prompt engineering, fine-tuning approaches and context management. Experience designing enterprise AI platforms and integrating AI capabilities with APIs, microservices, data platforms and existing business applications. Understanding of vector databases, knowledge retrieval architectures, distributed data processing and enterprise search technologies. Strong knowledge of cloud-native architecture and experience designing solutions across major cloud environments. Understanding of MLOps, LLMOps, model life cycle management, evaluation frameworks, observability and production monitoring. Knowledge of enterprise security architecture, identity and access management, data protection, responsible AI and regulatory considerations. Strong software engineering foundation, ideally with practical development experience in Python and familiarity with REST APIs, microservices and distributed systems. Ability to produce architecture designs, technical specifications, solution blueprints and implementation guidelines. Proven ability to provide technical leadership, influence architectural decisions and guide engineering teams through complex technology implementations. Desirable Qualifications and Skills Experience delivering AI or advanced analytics solutions within regulated, data-intensive or complex enterprise environments. Exposure to financial services, insurance or other industries with complex business processes and data requirements. Experience defining enterprise AI strategies, reference architectures, technology roadmaps and AI adoption frameworks. Knowledge of enterprise integration patterns and large-scale application modernisation. Familiarity with Java-based enterprise application development and associated Back End technologies. Experience establishing AI governance frameworks, architecture review processes and technology standards. Experience leading technical teams and working across architecture, engineering, data and business functions.