Lead AI Solution Architect
Location:
Remote within the UK
Contract Type:
Permanent
Role Overview We are seeking a Lead AI Solution Architect to act as the primary AI authority within our business domains. Embedded within the Enterprise Data Office Solution Architecture team, you will identify, design, and accelerate the delivery of production-grade ML, Generative AI, and Agentic AI solutions that create measurable business value.
Key Responsibilities
Architectural Leadership
* Serve as the trusted AI advisor from ideation through production.
* Apply enterprise Data, ML, and AI architecture principles pragmatically to deliver scalable, secure, and resilient solutions.
* Balance governance with enablement to accelerate delivery.
Solution Design & Engineering
* Partner with business leaders, product managers, and engineering teams to translate business needs into AI-driven products.
* Design end-to-end AI architectures including reusable AI services and domain-aligned capabilities.
* Define AI lifecycle frameworks (MLOps, LLMOps, AgentOps).
* Architect AI-aligned data patterns such as feature stores, RAG pipelines, and scalable inference services.
* Ensure solutions meet enterprise standards for security, reliability, and cost efficiency.
Enterprise Alignment & Governance
* Represent the business domain within enterprise architecture and AI communities.
* Contribute to Central Design Authority and architectural review forums.
* Ensure compliance with AI governance, risk, and security standards.
* Provide feedback to evolve enterprise platforms, blueprints, and guardrails.
Strategic Value & Innovation
* Identify high-impact AI opportunities aligned to business strategy.
* Advise on sequencing and prioritisation of ML and AI initiatives.
* Support the evolution of the domain's AI operating model and data product maturity.
Knowledge & Experience
* Deep expertise in modern AI architectures on Amazon Web Services and Databricks, including Generative AI, RAG, and large-scale inference workloads.
* Expert experience designing AI platforms with model serving, vector search, and foundation model integration.
* Strong knowledge of AI security architecture, including private endpoints, IAM least-privilege access, PII protection, and secure integration with external LLM providers.
* Proficiency in Infrastructure-as-Code (Terraform) and containerisation (Docker, Kubernetes) for scalable, GPU-optimised AI platforms.
* Hands-on application of MLOps and LLMOps practices, including automated evaluation, drift monitoring, CI/CD, and resilient real-time APIs.
* Experience managing AI FinOps trade-offs between proprietary and open-source LLMs.
* Demonstrated expertise in multi-agent orchestration and AI-native development workflows