Lead AI Architect

Salary and Benefits

• Competitive salary of up to £110,000
• Hybrid working (3 days in London office)
• Company pension scheme
• Private medical care
• And many more

As a Lead AI Architect, you will take ownership of the end‑to‑end AI architecture strategy, partnering with senior stakeholders to define roadmaps, evaluate technologies, and deliver scalable solutions that integrate seamlessly with enterprise systems.

You’ll collaborate closely with engineering, data, cloud, security, and product teams to ensure solutions are robust, compliant, and aligned with long‑term digital transformation plans.

• Translate senior stakeholder vision into AI/ML/GenAI architectural strategies and actionable implementation roadmaps.
• Design and recommend scalable end‑to‑end AI/ML architectures that integrate effectively with existing insurance and financial systems.
• Work closely with Data Scientists, MLOps/GenAI Engineers, Enterprise Architects, Dev Ops, and Business teams to pilot use cases and shape solution design.
• Select the right tooling and technology from a mix of open‑source and commercial platforms, ensuring compatibility with internal tooling and deployment patterns.
• Oversee successful delivery and continuous improvement of AI‑powered applications using agile methodologies.
• Partner with security and risk teams to ensure responsible and compliant AI adoption, aligned with emerging regulatory requirements.
• Support go‑to‑market activities, including proposal development and pricing input.
• Lead and mentor diverse technical teams, promoting an inclusive and supportive team culture.
• Develop junior talent through coaching, hands‑on guidance, and structured development programmes.

• Proven background in architecture disciplines with experience designing enterprise‑scale AI/Machine Learning solutions.
• Strong understanding of data science concepts and statistical foundations of AI/ML.
• Demonstrable experience architecting high‑performance, cost‑optimised solutions using serverless, containerised, or GPU‑enabled compute.
• Hands‑on experience deploying and hosting Large Language Models (LLMs).
• Knowledge of modern AI architectures and techniques including Transformers, RAG pipelines, vector databases, semantic search, and fine‑tuning.
• Proficient Python developer with experience using frameworks such as PyTorch, Tensor Flow, Lang Chain.
• Experience designing and implementing MLOps capabilities (CI/CD for ML, model monitoring, retraining workflows).
• Excellent stakeholder management skills, with the ability to influence and collaborate with multidisciplinary teams.
• Experience leading technology programmes and uniting cross‑functional teams behind a shared technical vision.