AI Platform Architect

Overview

In this role you will own the architecture of an enterprise AI platform spanning Azure and AWS, covering ML training, model hosting, MLOps, and Generative AI. You will define patterns for LLMs, RAG, and vector search, evaluating technologies to deliver secure, scalable, production-ready capabilities. You’ll guide ML and platform engineers, embedding governance and security to support regulated environments. This is a hands-on, technically deep leadership opportunity to shape enterprise AI in a major organisation.

Pay / Benefits

• bonus 25%
• hybrid/3 days in office
• London or Horsham location
• salary 90,000 to 95,000
• refer-a-friend up to 1,000 (terms apply)

Responsibilities

• Own end-to-end architecture of the 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 tooling based on technical, security and cost criteria
• Produce architecture blueprints, standards, reference architectures and design docs
• Design secure multi-cloud integration for data, models and services
• Provide technical direction to ML engineers and platform teams
• Embed security, governance, and regulatory requirements into the platform
• Optimize AI infrastructure for scale, performance, resilience and cost

Key requirements

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

• Clear communication and stakeholder management
• Technical leadership and ability to influence cross-functional teams
• Problem-solving mindset with governance mindset
• Azure ML
• Amazon SageMaker
• MLflow

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