Platform Architect

About the role

Capital.com is building out its internal platform capabilities, and AI is a growing part of that. The role combines architectural ownership with hands-on delivery: you will design, build, deploy and operate the platform, including its infrastructure and DevOps. Reporting to the Director of Product Security, you will set up this function as its first member and make the early technical decisions that the future team will build on.

As Platform Architect, you will own the architecture of the platform that more than 1,000 employees rely on, including their access to AI tools such as Anthropic Claude, OpenAI and future providers.

Responsibilities: -

Build the AI platform architecture: the reference design for how LLM capabilities are accessed, governed, monitored, and extended across the organisation.

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Own every architectural decision for the AI platform, and document, justify, and stand behind each one.

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Evaluate and select new models, tools, and frameworks as the market moves.

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Deliver the platform as a service, so teams can move their agents into governed cloud infrastructure.

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Govern Anthropic (Claude API, Claude Code, Anthropic Enterprise), OpenAI, and future providers, covering API access controls, credential management, model version policy, cost allocation, and acceptable use standards.

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Own technical relationships with Anthropic and OpenAI at the enterprise level.

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Build cloud infrastructure for AI workloads on AWS and GCP: Bedrock, SageMaker, Vertex AI, Cloud Functions, and supporting services (S3, Cloud Storage, IAM, and monitoring on both platforms).

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Deploy and securely configure n8n, and build automation workflows connecting LLMs to Jira, Confluence, Slack, Google Workspace, HR systems, and proprietary data sources.

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Design and secure MCP servers and gateways connecting AI agents to internal services.

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Build Python integrations for AI-powered internal tooling: RAG pipelines, agent frameworks, prompt libraries, and internal AI assistants.

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Deliver hands-on: you write the code, build the workflows, and deploy the tooling. This isn't an advisory role.

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Build data privacy and security architecture for LLM usage in a regulated environment, including prompt classification, output controls, and audit trails aligned with FCA, CySEC, ASIC, and DORA.

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Build observability across the full stack: token consumption, cost per team, latency, error rates, and policy violations across cloud platforms and direct API providers.

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Build an AI adoption framework of standards, patterns, and onboarding materials that let teams across Technology use AI safely and consistently.

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Run internal enablement through workshops, documentation, and direct support for teams moving from ad-hoc AI use to governed, repeatable processes.