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Salary
≈ $27k – $48k per year (Estimated)
Location
In office (Moscow)
Seniority
Senior · 5+ years exp

Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 31, 2026. Sber scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Sber is Russia's largest bank and the centre of one of the country's biggest technology ecosystems, tracing its origins to savings institutions established in 1841. It serves over a hundred million retail customers and several million businesses through branch, mobile and web channels, and holds a dominant share of the national deposit, lending and payment markets. Over the past decade the group has built out an ecosystem well beyond banking, spanning e-commerce, food delivery, streaming media, cloud infrastructure, devices and the GigaChat family of large language models developed by its own AI research teams.
Backed by Sber500

Наша команда ищет Senior Data Scientist, который будет заниматься разработкой ключевых GenAI-инициатив в канале ВСП.

Вам предстоит с нуля выстраивать end-to-end пайплайны: от исследования и прототипирования до вывода в продакшен интеллектуальных агентов и ML-моделей. Вы будете отвечать за полный цикл создания ценности - от бизнес-гипотезы до работающего продукта.

Обязанности

Что предстоит делать:

  • Проектировать и внедрять в продакшен мультиагентные системы (orchestrator-workers, ReAct, Plan-and-Execute).
  • Строить продакшен-RAG (гибридный поиск, ANN/HNSW, query rewriting, GraphRAG).
  • Разворачивать harness для агентов, настраивать guardrails, трейсинг траекторий и проводить A/B-тесты версий (промпт/модель/инструменты).
  • Оптимизировать LLM-пайплайны (DSPy / MIPROv2, управление контекстом и памятью).
  • Дообучать модели (SFT, LoRA/QLoRA) под ру-домены и внедрять function calling / MCP.

Требования

  • Релевантный опыт работы от 5 лет
  • Агенты: LangChain, LangGraph (обязательно); LlamaIndex, CrewAI (опционально).
  • ML/NLP: трансформеры, attention, prompt engineering (CoT, few-shot), Fine-tuning.
  • RAG и поиск: Qdrant, Elasticsearch или FAISS.
  • Оценка: LLM-as-a-Judge, Inspect AI / RAGAS / DeepEval, матстатистика для A/B-тестов.
  • Классический ML: CatBoost, XGBoost, скоринг, предотвращение утечек.
  • MLOps: Python (продакшен-код, ООП), Docker, k8s, CI/CD, MLflow/WandB, мониторинг data drift.
  • Инференс: vLLM, Triton, TensorRT/ONNX, квантизация, оптимизация latency и стоимости.

Условия

Ожидаемый стек и навыки:

  • Агенты: LangChain, LangGraph
  • ML/NLP: трансформеры, attention, prompt engineering (CoT, few-shot), Fine-tuning.
  • RAG и поиск: Qdrant, Elasticsearch или FAISS.
  • Оценка: LLM-as-a-Judge, Inspect AI / RAGAS / DeepEval, матстатистика для A/B-тестов.
  • Классический ML: CatBoost, XGBoost, скоринг, предотвращение утечек.
  • MLOps: Python, Docker, k8s, CI/CD, MLflow/WandB, мониторинг data drift.
  • Инференс: vLLM, Triton, TensorRT/ONNX, квантизация, оптимизация latency и стоимости.
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