Python Platform Engineer (AI InfoSec)
Role Overview
The role builds services, infrastructure, and tooling that enable teams to develop, deploy, and operate reliable AI agents in a regulated financial environment.
The role works with engineers, data scientists, and product owners to turn complex requirements into technical designs and production-ready systems.
Key Skills for This Role
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About Revolut
Revolut provides financial products and services including spending, saving, investing, exchanging, and travel.
The company describes a global workforce operating from offices and remotely.
Role overview
The role builds services, infrastructure, and tooling that enable teams to develop, deploy, and operate reliable AI agents in a regulated financial environment.
The role works with engineers, data scientists, and product owners to turn complex requirements into technical designs and production-ready systems.
What you will do
- Design, build, and maintain scalable Python services, APIs, and data pipelines for an internal agent platform.
- Develop and deploy LLM agents with tool integrations, orchestration, and state management.
- Build reusable capabilities for developing, testing, and operating production AI agents.
- Improve reliability, observability, performance, and cost efficiency through evaluation, monitoring, and error handling.
- Create technical designs and delivery plans for complex problems; review designs and code for security, maintainability, and business fit.
What you will need
- At least 5 years of backend-focused software engineering experience and 3 years using Python as the primary language.
- Hands-on LLM-agent development expertise and production experience with AI agent systems or platforms.
- STEM academic background; SQL fluency and proficiency in another object-oriented programming language.
- Experience with API development, integration, and distributed systems; ability to write concurrent code for I/O-bound and CPU-bound workloads.
- Familiarity with Docker, Kubernetes, CI/CD, monitoring, and alerting.
- Understanding of production agent challenges including tool execution, context management, evaluation, and failure handling.
- Ability to clarify ambiguous requirements, explain trade-offs, and deliver work through defined implementation steps.
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