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Salary
≈ $21k – $48k per year (Estimated)
Location
Remote (EAEU)
Seniority
Middle · 3+ years exp

Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Oct 22, 2026. MTS Bank scores B on the Alion truth index.

Overview
Company
Impact
Profile match
MTS Bank (MOEX: MBNK) is a major commercial bank and fintech platform headquartered in Moscow, Russia, operating as the digital financial services arm of telecom giant PJSC Mobile TeleSystems (MTS Group). Founded in 1993 (originally as the Moscow Bank for Reconstruction and Development before being rebranded in 2012), the institution is one of Russia's top 50 banks by assets, completing its initial public offering on the Moscow Exchange in 2024.

МТС Банк развивает цифровые финансовые сервисы и делает их доступными для всех.

Мы работаем с современным технологическим стеком, высоконагруженными системами и облачной инфраструктурой. Такой подход позволяет создавать надёжные решения для бизнеса и клиентов.

Мы в поиске Ведущего разработчика Python в команду ИИ-помощник.

Обязанности:

  • проектировать и разрабатывать backend-сервисы на Python (FastAPI / Flask / Django)
  • создавать и оптимизировать цепочки LangChain (chains, agents, tools, memory) для реализации сложных сценариев обработки запросов и выполнения многошаговых задач
  • выполнять интеграцию с LLM
  • разрабатывать архитектуру оркестратора: управление состоянием процессов, обработка ошибок и повторных попыток, логирование, аудит выполнения шагов
  • проектировать REST API для взаимодействия с фронтендом, мобильными клиентами и внешними системами
  • работать с диалоговыми сценариями ИИ-ассистента: контекстная память, маршрутизация намерений, вызов инструментов, генерация ответов
  • писать тесты (unit, integration), участвовать в code review, документировать API и внутренние сервисы

Требования:

  • Python 3.9+ - уверенное владение, опыт промышленной разработки от 3 лет
  • LangChain (или аналоги: LlamaIndex, Semantic Kernel) - практический опыт от 1 года: построение chains, agents, работа с memory, tools, output parsers
  • понимание принципов работы LLM: prompt engineering, few-shot, chain-of-thought, function calling, token limits, temperature и другие параметры
  • опыт интеграции с API LLM-провайдеров (OpenAI, Anthropic, Cohere и др.) и/или запуска open-source моделей
  • asyncio, fastapi или aiohttp
  • базы данных: PostgreSQL, Redis
  • микросервисная архитектура: Kubernetes, REST/gRPC, принципы проектирования отказоустойчивых сервисов
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