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Senior Full-Stack Engineer - AI Agent Platform

Оценка ИИ

Отличная вакансия для опытного инженера: работа с передовым стеком (AI/LLM), формат 'greenfield' проекта без легаси, сильная команда и офис в Лондоне. Высокий балл за инновационность и влияние на продукт.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Высокий уровень сложности обусловлен требованиями к глубоким знаниям как фронтенда (React), так и бэкенда (Python), а также необходимостью опыта работы с AI-инструментами (LangGraph, RAG) и облачной инфраструктурой AWS. Роль подразумевает техническое лидерство и менторство.

Анализ зарплаты

Медиана95 000 £
Рынок80 000 £ – 120 000 £
Оценка ИИ

Предлагаемая роль Senior Full-Stack инженера в Лондоне соответствует рыночному диапазону для финтех-сектора. Учитывая специфику AI и требования к 5+ годам опыта, зарплата в этом сегменте обычно находится в верхнем квартиле рынка.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Full-Stack Engineer position for the AI Agent Platform at Ebury. With over 6 years of experience in software engineering and a deep expertise in both React/TypeScript and Python/FastAPI, I am excited by the opportunity to build production-grade AI systems that transform financial crime investigations. My background in architecting scalable distributed systems and my passion for LLM orchestration using tools like LangGraph align perfectly with your team's mission.

In my previous roles, I have successfully led technical initiatives from design to production, mentoring junior developers and establishing robust engineering practices. I am particularly drawn to Ebury's 'startup-within-enterprise' environment and the chance to work on a greenfield AI product. I am confident that my technical leadership and experience with AWS infrastructure will allow me to make an immediate impact on your platform's reliability and scalability.

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Откликнитесь в ebury уже сейчас

Присоединяйтесь к Ebury, чтобы создавать инновационные AI-решения на стыке финтеха и комплаенса в самом сердце Лондона!

Описание вакансии

Ebury helps ambitious businesses unlock global growth, and we take the same approach with our people. We encourage innovation and movement, collaboration and problem-solving, and foster an environment where everyone can feel they belong, are valued, supported and empowered to succeed.

If you’re a collaborator who wants to help transform how businesses operate globally, get in touch - we’d love to discuss how Ebury can accelerate your career so you can shape the future.

Senior Full-Stack Engineer - AI Agent Platform

London Victoria Office - Hybrid: 4 days in the office, 1 day working from home

About the Role

We're building an AI-powered financial crime investigation platform that's transforming how Ebury's compliance teams work. Our AI agents will process thousands of screening cases, reducing analyst workload while maintaining regulatory standards.

We're looking for a Senior Full-Stack Engineer to help us scale our platform and raise the engineering bar. You'll architect and deliver complex features across the entire stack - from React frontends to Python backends to LLM integrations - while mentoring teammates and shaping our technical direction.

This is a unique opportunity to work at the intersection of AI/ML, fintech, and compliance - building production AI systems that handle real financial crime investigations at scale.

What You'll Do

  • Architect and build end-to-end features across our React frontend and FastAPI backend, making sound technical decisions that balance speed with maintainability
  • Design AI agent workflows using LangGraph for screening automation, RAG pipelines, and multi-agent orchestration
  • Lead technical initiatives - own complex features from design to production, breaking down ambiguous problems into actionable work
  • Integrate complex data sources (internal APIs, third-party services like LexisNexis, data warehouse) with robust error handling and observability
  • Improve platform reliability - implement monitoring, alerting, and performance optimizations for production AI systems
  • Mentor and uplift junior and mid-level engineers through code reviews, pairing, and technical guidance
  • Shape engineering practices - contribute to architecture decisions, coding standards, and team processes
  • Collaborate cross-functionally with Product, Design, Compliance, and other engineering teams to deliver impactful solutions

Tech Stack

Frontend:

  • React, TypeScript, Material-UI
  • Module Federation (Micro-frontend architecture)
  • Vite, Vitest

Backend:

  • Python 3.13, FastAPI
  • LangGraph (AI agent orchestration)
  • LlamaIndex (RAG/vector search)
  • AWS Bedrock
  • DynamoDB, OpenSearch Serverless

Infrastructure:

  • AWS (ECS Fargate, Lambda, API Gateway, S3)
  • Terraform, GitHub Actions
  • Docker, Kubernetes
  • Sentry, Langfuse (LLM observability)

What We're Looking For

Must Have:

  • 5+ years of professional software engineering experience
  • Strong React + TypeScript skills - you can architect complex frontend applications with good state management, performance, and testing
  • Strong Python backend experience - FastAPI, Django, or Flask with async programming, clean architecture, and production-grade code
  • System design skills - you can design scalable, maintainable systems and articulate trade-offs clearly
  • API design expertise - REST, WebSockets/SSE, and understanding of distributed systems patterns
  • Database proficiency - SQL and NoSQL (DynamoDB, PostgreSQL, OpenSearch), including query optimization and data modeling
  • Testing mindset - you write comprehensive tests and advocate for quality across the team
  • Ownership mentality - you take features from idea to production, proactively identifying and solving problems
  • Strong communication - you can explain complex technical concepts to engineers and non-engineers alike, and write clear documentation
  • Mentorship ability - you enjoy helping others grow and have experience guiding less experienced engineers

Nice to Have:

  • LLM/AI experience - LangChain, LangGraph, RAG pipelines, prompt engineering, or fine-tuning
  • Production AI systems - experience with LLM observability (Langfuse), token management, streaming, and reliability patterns
  • AWS expertise - Lambda, ECS, DynamoDB, OpenSearch, Bedrock, or similar cloud services
  • Streaming architectures - SSE, WebSockets, event-driven systems
  • Fintech/compliance background - AML, sanctions screening, KYC, or regulated industry experience
  • Micro-frontend experience - Module Federation, independent deployability
  • Infrastructure as Code - Terraform, CDK, or similar

What Sets Senior Apart

We're looking for engineers who demonstrate:

  • Technical leadership - You don't just complete tasks; you identify the right problems to solve and influence technical direction
  • Systems thinking - You consider the broader context: scalability, security, observability, and maintainability
  • Proactive communication - You surface risks early, propose solutions, and keep stakeholders informed
  • Quality advocacy - You raise the bar for testing, code review, and documentation across the team
  • Pragmatic decision-making - You balance ideal solutions with delivery timelines, making conscious trade-offs
  • Continuous learning - You stay current with industry trends and bring new ideas to the team

Why Join Us?

  • Greenfield AI Product: You'll help shape a product from early stages, not maintain legacy code. Your architectural decisions will have lasting impact.
  • Technical Leadership: Drive technical direction for a growing team. Mentor engineers and establish patterns that scale.
  • Cutting-Edge AI: Work with the latest LLM technologies in production - LangGraph, RAG, multi-agent systems, and more.
  • Real Impact: Our AI agents process thousands of compliance cases. You'll see your work making a difference within weeks.
  • Startup-within-Enterprise: Small, autonomous team with startup speed, backed by enterprise resources and stability.

About us

Ebury delivers sophisticated, integrated solutions — business accounts, hedging, and financing — on a single platform with a seamless workflow. Our success is built on a simple premise and singular purpose: To help businesses operate and scale globally.

Since its founding in 2009, Ebury has always been a fast-growing leader in fintech. Today, we bring together 1,800+ Eburians across nearly 70 cities and we’re always looking to add to our team.

At the heart of our offering is a proprietary platform, purpose-built to help businesses seamlessly streamline and manage global cash flow. We focus on continuous product evolution and innovation to build the infrastructure for borderless growth and help our clients scale at every stage.

The opportunities at Ebury are as diverse as our people, ranging from business development to engineering roles across our tech pillars.

We believe in inclusion. We stand against discrimination in all forms and are against the intolerance of differences that makes us a modern and successful organisation. At Ebury, you can be whoever you want to be and still feel a sense of belonging no matter your story.

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Навыки

  • TypeScript
  • AWS
  • Python
  • Terraform
  • Kubernetes
  • React
  • Docker
  • DynamoDB
  • FastAPI
  • LangGraph
  • Micro-frontends
  • OpenSearch
  • Material UI
  • LlamaIndex
  • Vite

Возможные вопросы на собеседовании

Проверка опыта работы с ключевым инструментом стека для управления логикой AI-агентов.

Расскажите о вашем опыте проектирования рабочих процессов AI-агентов с использованием LangGraph или аналогичных библиотек. С какими основными трудностями вы сталкивались при обеспечении предсказуемости ответов?

Оценка навыков проектирования сложных систем, что критично для Senior-позиции.

Как бы вы спроектировали архитектуру микрофронтендов с использованием Module Federation для платформы, требующей высокой степени независимости команд?

Проверка умения работать с асинхронностью в Python, что указано в требованиях.

В чем заключаются основные риски и преимущества использования асинхронного программирования в FastAPI при интеграции с внешними API и базами данных?

Оценка опыта работы с облачной инфраструктурой и понимания стоимости/производительности.

Какие стратегии масштабирования вы бы предложили для системы на базе AWS ECS Fargate, обрабатывающей тысячи проверок в реальном времени?

Проверка лидерских качеств и умения развивать команду.

Опишите случай, когда вам пришлось принимать сложное архитектурное решение, с которым была не согласна часть команды. Как вы аргументировали свою позицию и каков был результат?

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ebury
Страна
Великобритания