Platform Architect

Capital
Warsaw +1 more
Hybrid

Who this role is best for

Geared toward self-driven engineers comfortable with AI platform governance and multi-cloud infrastructure. Best suited for candidates with fintech or regulated industry experience and a preference for hands-on technical leadership in a small-team or solo setup.

Best fit for

  • Candidates with 8+ years of engineering experience and a track record of deploying LLM systems in production
    — “8+ years in engineering, with at least 2 years building LLM-based systems in production”
  • Individuals who have operated as the first person in a new function and can set priorities independently
    — “Comfort operating as the first person in a function: you set your own priorities”
  • Professionals with a strong background in cloud infrastructure and AI governance frameworks
    — “Build data privacy and security architecture for LLM usage in a regulated environment”

Things to consider

  • Requires a high level of technical autonomy and ownership in a nascent function
    — “you set your own priorities, unblock yourself, and deliver without a team around you”
  • Candidate must be prepared to take full technical and strategic responsibility for AI platform decisions
    — “Own every architectural decision for the AI platform, and document, justify, and stand behind each one”

How to stand out

  • Demonstrate experience in securing AI agent flows and internal service gateways
    — “Design and secure MCP servers and gateways connecting AI agents to internal services”
  • Showcase hands-on Python development for internal AI tooling and integration pipelines
    — “Build Python integrations for AI-powered internal tooling: RAG pipelines, agent frameworks”
  • Highlight your ability to build and operate observability metrics across cloud platforms
    — “Build observability across the full stack: token consumption, cost per team, latency”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design and build the AI platform architecture
  • Govern Anthropic and OpenAI APIs
  • Deploy and secure cloud infrastructure
  • Build observability across the full stack
  • Run internal enablement workshops
Typical background
8+ years in engineeringExperience building LLM-based systems in production

Skills & requirements

Required

Llm-based SystemsPythonAnthropic And Openai ApisCloud InfrastructureDevOpsData Privacy And SecurityObservability

Preferred

Agentic PlatformsMCP ServersMCP GatewaysRAG PipelinesPrompt LibrariesInternal AI Assistants

Stack & domain

PythonAnthropic ClaudeOpenaiAWSGCPN8nJiraConfluenceSlackGoogle WorkspaceHR SystemsProprietary Data SourcesMCP ServersMCP GatewaysAgentic PlatformsLLM Orchestration FrameworksLangchainLlamaindexLLM Guardrail And DLP ToolingBedrock GuardrailsVertex AI Safety FiltersPrompt-firewallPii-redaction ToolsInternal Developer PlatformsAI Centres Of ExcellenceAWS Solutions Architect ProfessionalGoogle Professional ML EngineerLeadershipCommunicationProblem-solvingTeamworkAdaptabilityTime ManagementDecision-makingTechnologyAICloud InfrastructureDevOpsData PrivacySecurity

About the role

Original posting from Capital via Lever

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.

-

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

Requirements::

-

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.

Nice to have::

-

Fintech, brokerage, or financial services background.

-

DORA or ISO 27001:2022 knowledge as it applies to AI system risk.

-

Familiarity with the EU AI Act and other AI regulatory requirements, as well as AI-specific risk and governance standards such as NIST AI RMF or ISO/IEC 42001.

-

LLM orchestration frameworks: LangChain, LlamaIndex, or similar.

-

Hands-on experience with LLM guardrail and DLP tooling: Bedrock Guardrails, Vertex AI safety filters, or prompt-firewall and PII-redaction tools.

-

Experience building internal developer platforms or AI centres of excellence.

-

AWS Solutions Architect Professional, Google Professional ML Engineer, or equivalent.

Grow with Capital.com and help shape how millions of people access the markets.

Source: Capital careers (Lever)

Similar roles