AI Delivery Architect

Queen Square Recruitment; Greater London; On-site; GBP 110,000 - 150,000; Full time

Job summary

Queen Square Recruitment Ltd is seeking an experienced AI Delivery Architect to lead the architecture and technical delivery of enterprise AI solutions. This hands-on role translates business requirements into robust designs and partners with engineering teams to move AI from proof-of-concept to production.

You will own architectural decisions across models, data, integrations, cloud, security, evaluation and operations, ensuring scalable, secure and cost-effective solutions.

Qualifications

• 10-15 years of technology experience, including solution architecture and delivery.
• Hands-on delivery of enterprise AI solutions beyond proof-of-concept or experimental projects.
• Strong understanding of LLMs, generative AI, RAG, context engineering and agentic AI patterns.
• Experience with at least one major cloud platform: AWS, Azure or Google Cloud.
• Knowledge of containers, Kubernetes, infrastructure as code, CI/CD, identity, secrets management and monitoring.
• Proven ability to lead multidisciplinary engineering teams and communicate architectural trade-offs to technical and business stakeholders.
• Experience in regulated, security-sensitive or high-availability environments.
• Exposure to multi-model, hybrid, on-premises or sovereign AI environments.
• Relevant cloud architecture, AI, data or security certifications.
• Experience designing enterprise-grade applications, APIs, data platforms and integrations.

Responsibilities

• Design end-to-end enterprise AI solutions covering applications, LLMs, data, retrieval, integrations, security, deployment and ongoing operations.
• Translate business and domain requirements into technical architectures, delivery roadmaps, architecture decision records and non-functional requirements.
• Design and guide the implementation of generative AI, retrieval-augmented generation (RAG) and agentic AI solutions.
• Define patterns for agent orchestration, tool calling, memory, context management and human-in-the-loop approvals.
• Architect secure integrations with enterprise systems using APIs, microservices, events, queues and other integration patterns.
• Establish standards for AI evaluation, testing, observability, performance, cost management and production readiness.
• Guide engineering teams through architecture reviews, technical design discussions and critical implementation reviews.
• Address technical risks across applications, data, integrations and cloud platforms.
• Promote reusable architectures, engineering standards, documentation and operational best practices.
• Support knowledge transfer to ensure solutions can be maintained and extended by wider delivery teams.

Skills

LLMs & Gen AI

RAG & Agentic AI

Cloud Platforms

Kubernetes & Containers

IaC & CI/CD

Security & Privacy

Leadership & Stakeholders

Enterprise APIs

Job description

We are seeking an experienced AI Delivery Architect to lead the architecture and technical delivery of enterprise AI solutions. This is a hands-on role for someone who can translate business requirements into robust, implementable designs and work closely with engineering teams to take AI solutions beyond proof-of-concept and into production.

You will own key architectural decisions across AI models, data, integrations, cloud infrastructure, security, evaluation and operations, ensuring solutions are scalable, reliable, secure and cost-effective.

Key responsibilities

• Design end-to-end enterprise AI solutions covering applications, LLMs, data, retrieval, integrations, security, deployment and ongoing operations.
• Translate business and domain requirements into technical architectures, delivery roadmaps, architecture decision records and non-functional requirements.
• Design and guide the implementation of generative AI, retrieval-augmented generation (RAG) and agentic AI solutions.
• Define patterns for agent orchestration, tool calling, memory, context management and human-in-the-loop approvals.
• Architect secure integrations with enterprise systems using APIs, microservices, events, queues and other integration patterns.
• Establish standards for AI evaluation, testing, observability, performance, cost management and production readiness.
• Guide engineering teams through architecture reviews, technical design discussions and critical implementation reviews.
• Address technical risks across applications, data, integrations and cloud platforms.
• Promote reusable architectures, engineering standards, documentation and operational best practices.
• Support knowledge transfer to ensure solutions can be maintained and extended by wider delivery teams.
• 10-15 years of overall technology experience, including substantial solution architecture and technical delivery experience.
• Demonstrable hands-on delivery of enterprise AI solutions beyond proof-of-concept or experimental projects.
• Strong understanding of LLMs, generative AI, RAG, context engineering and agentic AI patterns.
• Experience designing enterprise-grade applications, APIs, data platforms and integrations.
• Practical experience with at least one major cloud platform: AWS, Microsoft Azure or Google Cloud.
• Knowledge of containers, Kubernetes, infrastructure as code, CI/CD, identity, secrets management and monitoring.
• Experience defining AI evaluation strategies, testing standards, observability and production controls.
• Understanding of responsible AI, security, privacy, reliability, resilience and cost optimisation.
• Proven ability to lead multidisciplinary engineering teams and communicate architectural trade-offs to technical and business stakeholders.
• Experience in regulated, security-sensitive or high-availability environments.
• Experience establishing reusable AI platform patterns or agent orchestration standards.
• Exposure to multi-model, hybrid, on-premises or sovereign AI environments.
• Relevant cloud architecture, AI, data or security certifications.

What we are looking for

A delivery-focused architect who combines strong technical judgement with practical implementation experience. You should be comfortable making decisions in ambiguous environments, reviewing real implementations and taking accountability for the technical quality of solutions entering production.