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Platform Architect

Capital.com
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Hybrid (Warsaw; Limassol; Sofia)HybridPosted 4d ago

About the role

Capital.com is building out its internal platform capabilities with a focus on AI. The Platform Architect will own and deliver the AI platform architecture, provide hands-on delivery, and establish the function as its first member, setting technical direction for the future team.

Responsibilities

  • Build the AI platform architecture: the reference design for how LLM capabilities are accessed, governed, monitored, and extended across the organisation.
  • Own every architectural decision for the AI platform, and document, justify, and stand behind each one.
  • Evaluate and select new models, tools, and frameworks as the market moves.
  • Deliver the platform as a service, so teams can move their agents into governed cloud infrastructure.
  • 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.
  • Own technical relationships with Anthropic and OpenAI at the enterprise level.
  • 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).
  • Deploy and securely configure n8n, and build automation workflows connecting LLMs to Jira, Confluence, Slack, Google Workspace, HR systems, and proprietary data sources.
  • Design and secure MCP servers and gateways connecting AI agents to internal services.
  • Build Python integrations for AI-powered internal tooling: RAG pipelines, agent frameworks, prompt libraries, and internal AI assistants.
  • Deliver hands-on: you write the code, build the workflows, and deploy the tooling. This isn't an advisory role.
  • 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.
  • Build observability across the full stack: token consumption, cost per team, latency, error rates, and policy violations across cloud platforms and direct API providers.
  • Build an AI adoption framework of standards, patterns, and onboarding materials that let teams across Technology use AI safely and consistently.
  • Run internal enablement through workshops, documentation, and direct support for teams moving from ad-hoc AI use to governed, repeatable processes.
  • Report AI platform status, risks, and roadmap to Technology leadership

Qualifications

  • 8+ years in engineering, with at least 2 years building LLM-based systems in production, not prototypes or POCs.
  • Comfort operating as the first person in a function: you set your own priorities, unblock yourself, and deliver without a team around you.
  • Production-grade Python: tooling that others depend on, not scripts you hand off.
  • Deep, hands-on experience with the Anthropic and/or OpenAI APIs: function calling, tool use, streaming, RAG, prompt management, token optimisation, and model versioning.
  • Hands-on experience with agentic platforms, MCP servers, and MCP gateways, including designing flows between AI agents and internal services.
  • n8n: deploying, securely configuring, and maintaining multi-step automation workflows at organisation scale.
  • Solid AWS for AI workloads: Bedrock, SageMaker, Lambda, S3, IAM, and CloudWatch, deployed and operated in production.
  • Solid GCP for AI workloads: Vertex AI, Cloud Functions, Cloud Storage, IAM, and Cloud Monitoring.
  • Hands-on DevOps capability to deploy and run what you build.
  • Architectural thinking: end-to-end system design, clear trade-off documentation, and the ability to defend decisions to senior stakeholders.
  • Security engineering for what you build: secrets and credential management, key rotation, and least-privilege IAM across cloud and SaaS.
  • Data protection and auditability by design: classifying what data can reach which model and provider, controlling and logging prompts and outputs, and building audit trails that hold up to a regulator.
  • Fluency in the LLM threat surface, including prompt injection, data exfiltration through model outputs, insecure tool and agent use, and model supply-chain risk, with working knowledge of a recognised framework such as the OWASP LLM Top 10 or NIST AI RMF.

Skills

Required:Python (production-grade)
Last updated Oct 21, 2026. Interested? Apply on the company site.

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