AI Platform Architect

This is an opportunity to own the architecture of an enterprise AI platform spanning Azure and AWS, supporting everything from traditional machine learning to Generative AI, LLMs, and emerging AI technologies.

The business is looking for a technically strong architect to define the platform’s direction, evaluate technologies and work closely with engineering teams to turn architectural designs into secure, scalable, production-ready capabilities. Key Responsibilities of the AI Platform Architect

• Own the end-to-end architecture of an enterprise AI platform across Azure and AWS.
• Design capabilities for ML training, model hosting, MLOps and Generative AI.
• Define architecture patterns for LLMs, RAG, vector search and agent-based frameworks.
• Evaluate AI technologies and select appropriate tooling based on technical, security and cost requirements.
• Produce architecture blueprints, technical standards, reference architectures and design documentation.
• Design secure multi-cloud integration patterns covering data, models and services.
• Provide technical direction to ML engineers, platform engineers and wider delivery teams.
• Embed security, responsible AI, governance and regulatory requirements into the platform.
• Optimise AI infrastructure for scalability, performance, resilience and cost., The successful AI Platform Architect will have significant ownership of the technical direction of an enterprise AI platform, working closely with AI engineering, security and enterprise architecture stakeholders.

• Proven experience designing and owning enterprise-scale AI or ML platforms.
• Strong architecture experience across Azure and AWS, including multi-cloud integration, networking, IAM and security.
• Deep understanding of MLOps, model training pipelines, orchestration, feature stores, model hosting and observability.
• Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails.
• Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks.
• Ability to evaluate technologies and make evidence-based architectural decisions.
• Experience designing secure, scalable and cost-optimised AI infrastructure.
• Strong architecture documentation skills, including reference architectures and capability models.
• Ability to guide technical delivery, influence engineering teams and resolve complex architecture challenges.
• Experience within financial services, insurance or another regulated industry would be advantageous.