platform engineer

Enfint; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Kарта Pleo

Обеденный бонус/питание на работу

Медицинское страхование, в зависимости

Годовых 25 дней отпуска

Job summary

Pleo ищет квалифицированного инженера backend для проектирования и эксплуатации ключевых компонентов GenAI платформы. Ваша роль включает разработку маршрутов LLM, векторного поиска, RAG‑инфраструктуры и инструментов для безопасной разработки.

Вы будете владеть функционалом на продакшн и работать с командами Applied AI и продуктом, обеспечивая высокую устойчивость и качество сервисов. Требуется опыт разработки и эксплуатации продакшн‑сервисов, знание JVM‑языков или Python, а также умение работать

Qualifications

• Требуется сильный опыт разработки бэкенда и эксплуатации продакшн‑сервисов с требованиями к надёжности и наблюдаемости.

Responsibilities

• Разработка, сборка и эксплуатация ключевых компонентов GenAI платформы, включая маршрутизатор LLM, векторный поиск и инфраструктуру RAG.
• Обеспечение выпусков функционала от дизайна до развёртывания и мониторинга.
• Повышение надёжности через мониторинг, алерты, ретриви и обработку инцидентов.

Skills

Backend-разработка

Системная инженерия

Обеспечение наблюдаемости

Опыт работы с LLM API

Кросс-командное взаимодействие

Tools

JVM-языки (Java/Kotlin)

Python

Job description

Описание:

Пleo builds spend management solutions that help finance teams and employees manage business spending more seamlessly and effectively.

Задачи:

• Design, build, and operate core GenAI platform components, including an LLM routing gateway, vector search and RAG infrastructure, tool registry and MCP gateway, AI observability and evaluation tooling, and infrastructure for long-running agentic workflows;
• Own production-quality delivery of platform features from design through rollout, monitoring, and follow-up;
• Contribute to resilient system design with sensible APIs, failure handling, rate limiting, retries, idempotency, and safe change management;
• Improve reliability and observability through metrics, dashboards, alerting, incident follow-ups, and operational improvements;
• Partner with Applied AI Engineers and product teams to understand platform needs and help them build AI-powered features safely;
• Build internal SDKs, templates, and guardrails for product engineers;
• Support other engineers through pairing, code reviews, technical feedback, and clear documentation;
• Help evaluate build-versus-buy decisions in the LLMOps tooling landscape;
• Develop a clear picture of how AI features are built at Pleo and identify infrastructure bottlenecks;
• Take ownership of a core platform component and improve its reliability, observability, or developer experience;
• Deliver production-ready improvements with rollout plans, monitoring, and operational documentation;
• Partner with Applied AI Engineers and product teams to identify platform investments;
• Contribute to Pleo's internal standards for AI feature development, quality evaluation, prompt management, and production monitoring.

Требования:

• Strong backend and systems engineering background with experience building and operating production services with reliability and observability requirements;
• Experience designing and delivering shared platform or infrastructure components used by multiple teams;
• Strong production ownership, including monitoring, alerting, incident response, debugging, and post-incident learning;
• Knowledge of distributed systems fundamentals, including asynchronous workflows, idempotency, consistency trade-offs, and designing for failure;
• Hands-on experience with LLM APIs or strong interest in learning about rate limits, context windows, multi-vendor routing, latency variance, and cost control;
• Security mindset for AI systems, including prompt injection risks, PII in logs, data leakage, and safe credential handling;
• Strong programming experience in a JVM-based language or Python, with the ability to contribute to Kotlin and Python components;
• Clear communication and collaboration skills for turning ambiguous platform needs into practical solutions;
• Passion for developer experience and enabling engineering teams;
• Deep understanding of LLMOps, data retrieval, prompt and context engineering, and model evaluation in production;
• Ability to work across languages and technologies to achieve goals;
• Ability to explain AI trade-offs clearly to non-technical stakeholders;
• Proven experience building platforms or tooling for agentic AI;
• Ability to work fully autonomously on an entire product feature from design to implementation;
• Candidates primarily interested in model research or algorithm development are not a fit;
• Candidates who prefer building customer-facing features are not a fit.

Условия:

• Remote, hybrid, or in-person work options are available depending on the location;
• The employee must be physically based in the chosen country with a valid right to work;
• Visa sponsorship is not available;
• Pleo card provided;
• Catered meals or a lunch allowance is available for work days;
• Comprehensive private healthcare is provided depending on location;
• 25 Days of holiday plus public holidays;
• Free mental health and well-being support through MyndUp;
• Paid parental leave;
• The interview process includes an intro call, technical screening, system design interview, live coding interview, Hiring Manager interview, and final leadership interview;