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# AI Platform Architect

- **Company:** RSA Group
- **Location:** UK
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Architectural Patterns, Microsoft Azure, Encodings, Identity and Access Management, Machine Learning, Azure Machine Learning, Software Safety, Feature Store, Retrieval-Augmented Generation, Large Language Models, Multi-Cloud, AI Platforms, Machine Learning Operations, Cloud Integration
- **Published:** October 17, 2026
- **Apply:** https://www.rsagroup.com/careers/search-our-vacancies/uk-job-description?jobId=188275

## About the Role

* Experience designing enterprise-grade AI or ML platforms across Azure and AWS.
 * Strong knowledge of ML/AI platform components, including pipelines, orchestration, feature stores, model hosting and observability.
 * Expertise in GenAI architecture, including LLM hosting, vector databases, RAG patterns, guardrails and evaluation frameworks.
 * Solid understanding of multi-cloud architecture, networking, IAM, security and cross-cloud integration.
 * Experience comparing AI/ML tooling and making clear, evidence-based architectural recommendations.
 * Awareness of responsible AI, data-protection and regulatory expectations relevant to AI/ML.
 * Skilled in producing high-quality architectural models and documentation.
 * Experience designing scalable, cost-efficient training and inference architectures.
 * Ability to troubleshoot complex issues and support engineering and ML teams.
 * Background in enterprise or regulated environments (e.g., financial services) is beneficial.

## Description

* Defining and owning the architecture for a secure, scalable multi-cloud AI Platform across Azure and AWS.
 * Designing key platform capabilities, including ML pipelines, model hosting, observability, GenAI patterns and AI safety controls.
 * Evaluating AI tooling and making evidence-based technology decisions that balance engineering needs, security, governance and cost.
 * Creating clear architectural standards, blueprints and documentation to guide consistent platform development.
 * Enabling seamless integration across cloud environments and internal systems to support data, model and service interoperability.
 * Supporting AI use case delivery by ensuring solution designs align with platform capabilities and governance requirements.
 * Providing architectural oversight during implementation and embedding responsible AI, security and compliance expectations.
 * Driving cost-efficient, reliable and resilient design across training, inference and data workflows.
 * Representing the AI Platform in governance forums and aligning designs with wider cloud, data and enterprise architecture strategies.
 * Partnering with engineering, data science and business teams to ensure the platform meets diverse and evolving user needs.