Lead / Principal AI Engineer

At AKQA (part of WPP), we believe in the imaginative application of art and science to design a better future. Our Technology teams combine engineering excellence, creativity and innovation to build intelligent digital products, platforms and experiences for some of the world's most ambitious brands.

As a Lead / Principal AI Engineer, you'll design, build and deliver production-grade AI systems, intelligent workflows and agentic solutions that power modern digital experiences. From consumer-facing applications and connected digital products to intelligent marketing platforms and bespoke enterprise tools, you'll turn emerging AI capabilities into reliable, scalable and commercially valuable solutions.

This is a highly hands-on engineering role within AKQA's London studio, combining deep technical expertise with architecture and technical leadership. You'll spend the majority of your time designing and building solutions, while also working closely with clients, creative teams, strategists and engineers to translate ambitious ideas into production-ready technology.

You'll bring a strong background in backend or platform engineering, alongside recent, practical experience delivering generative AI and agentic systems into live environments. You'll understand that building a successful AI solution goes far beyond a prototype, requiring robust architecture, evaluation, security, observability and careful management of cost, performance and reliability.

ROLE REQUIREMENTS

• Architect, develop and deploy production-ready AI systems, including agentic workflows, multi-agent orchestration, retrieval-augmented generation (RAG) and intelligent automation solutions.
• Lead the technical design and implementation of AI-powered digital products, applications and platforms, ensuring solutions are scalable, secure, reliable and maintainable.
• Design and integrate agent tools, APIs and Model Context Protocol (MCP) interfaces, enabling secure and efficient interaction with enterprise systems, databases and external services.
• Develop compound AI architectures combining large language models with specialised models, semantic search, classification, re-ranking and deterministic workflows to improve accuracy, performance and cost efficiency.
• Build robust evaluation frameworks, automated testing, guardrails and observability capabilities to monitor model behaviour, output quality, hallucinations, latency and operational performance.
• Apply strong engineering judgement to model selection, prompt engineering, structured outputs, fine-tuning and retrieval strategies, balancing technical requirements with commercial objectives.
• Design and develop backend services, APIs and integration layers using Python, TypeScript, Node.js and modern cloud technologies.
• Collaborate with front-end, mobile and product engineering teams to integrate AI capabilities into modern web applications, including React, Next.js and headless digital platforms.
• Implement effective strategies for managing token usage, caching, latency, rate limits, fallback mechanisms and graceful degradation across AI services.
• Establish CI/CD practices for AI systems, incorporating automated evaluations, regression testing, version control and reliable deployment processes.
• Work with cloud and model platforms such as Azure OpenAI, AWS Bedrock, Google Cloud Vertex AI, OpenAI and Anthropic to design and deliver enterprise-ready solutions.
• Partner with creative, strategy, UX and technology teams to explore innovative applications of AI and translate concepts into technically feasible solutions.
• Provide technical leadership and architectural guidance to engineering teams, promoting high standards of software quality, security and engineering best practice.
• Engage with clients and senior stakeholders to communicate technical approaches, explain architectural decisions and shape solutions that deliver meaningful business value.

QUALITIES AND CHARACTERISTICS

• Significant experience in backend, platform or software engineering, with a strong understanding of distributed systems, microservices, asynchronous processing, API architecture and cloud infrastructure.
• Proven hands-on experience designing, deploying and maintaining generative AI or agentic systems in production, with a strong track record of delivering commercially deployed AI engineering solutions.
• Advanced proficiency in Python and/or TypeScript/Node.js, with a commitment to writing clean, testable and maintainable production code.
• Strong practical understanding of LLM architectures, agentic workflows, tool calling, RAG, embeddings, vector databases and hybrid retrieval techniques.
• Experience with agent orchestration frameworks such as LangGraph, CrewAI, AutoGen, Vercel AI SDK or custom-built orchestration solutions.
• Demonstrable experience developing AI evaluation and observability capabilities, using tools such as LangSmith, Arize Phoenix, Braintrust, Helicone or OpenTelemetry.
• Strong understanding of software architecture, security, authentication, data management and enterprise integration patterns.
• Experience implementing automated testing and CI/CD pipelines, including unit, integration and regression testing, with an understanding of how to evaluate non-deterministic AI systems.
• Practical knowledge of cloud infrastructure and AI model ecosystems, including the operational considerations involved in deploying and scaling intelligent applications.
• Understanding of modern digital product development, including web applications, headless CMS platforms, APIs and front-end technologies.
• Ability to balance technical ambition with practical considerations around cost, latency, performance, security and maintainability.
• A curious and pragmatic approach to emerging technologies, with the ability to distinguish meaningful innovation from experimentation and identify where AI can deliver genuine value.
• Confident communicating complex technical concepts to clients and non-technical stakeholders, contributing to solution design and technical decision-making.
• Comfortable taking ownership of technical architecture while remaining actively involved in hands-on engineering and delivery.
• Experience working within a digital agency, technology consultancy or product-led environment, collaborating across multidisciplinary teams and managing client expectations.
• Exposure to fine-tuning smaller or open-weight models, such as Llama or Mistral, for domain-specific applications would be advantageous.
• Experience with design-to-code workflows, Figma integrations, design systems or generative user interfaces would be beneficial.
• A collaborative, proactive and technically curious individual who enjoys solving complex problems and delivering high-quality engineering solutions.

Explore our extensive range of benefits here.