AI Architect - Generative AI & Agentic AI (Hybrid working 3 days in the office per week)

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.