CA
CapitalPlatform Architect
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Before you apply
LocationWarsaw, Mazowieckie, Poland
Work modehybrid
Typefull-time
SeniorityLead
Experience8+ yrs
DepartmentTechnology, Security - AppSec
Company size501–1,000 people
PostedOct 16, 2026 · 3w ago
Verified live21h ago
At a glanceSummarised by Seekless from the posting.
8+ years in engineering, with at least 2 years building LLM-based systems in production
Comfort operating as the first person in a function
Production-grade Python
Deep, hands-on experience with the Anthropic and/or OpenAI APIs
Hands-on experience with agentic platforms, MCP servers, and MCP gateways
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
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
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
Skills
Python
Anthropic Claude
OpenAI
n8n
AWS
GCP
Bedrock
SageMaker
Vertex AI
Cloud Functions
S3
Cloud Storage
IAM
CloudWatch
Cloud Monitoring
LangChain
LlamaIndex
Jira
Confluence
Slack
Google Workspace
MCP
OWASP LLM Top 10
NIST AI RMF
DORA
ISO 27001:2022
EU AI Act
ISO/IEC 42001
Bedrock Guardrails
Vertex AI safety filters
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.
•
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.
About the company
Grow with Capital.com and help shape how millions of people access the markets.