Description
We develop agentic RAG systems that help the company enhance customer experience through personalized solutions.
We already have a working product operating in production and delivering value. The team is currently actively working on new applications of the technology and improving the quality in existing operational areas.
Responsibilities
- Develop, optimize, and maintain NLP pipelines, including RAG systems and assistants for business tasks
- Create and develop AI agents and multi-agent systems (workflow orchestration, planning, tools, memory modules, integrations with bank services)
- Integrate agentic pipelines into the bank's high-load services, ensuring stability, performance, and monitoring
- Develop services around models: API layers, microservices, inference scripts, CI/CD for ML
- Ensure code quality and oversee engineering practices (testing, logging, monitoring).
Requirements
- Knowledge of the fundamentals of classical ML and NLP/LLM: classical tasks and architectures/approaches for solving them, metrics, the model training process
- Experience in development and production implementation of RAG systems, AI assistants, working with vector databases and the retrieval stack
- Excellent knowledge of Python, experience writing industrial, maintainable, and testable code, working with concurrency and asynchronicity.
Will be a plus:
- Knowledge of CI/CD, proficiency with development and infrastructure tools: Docker/Openshift/Kubernetes/Jenkins/Prometheus/Grafana.
Conditions
- Comfortable modern office near Kutuzovskaya metro station
- Work format - hybrid possible after probation period
- Annual salary review, annual bonus
- Corporate gym and relaxation areas
- More than 400 educational programs from SberUniversity for professional and career development
- Extended VHI, preferential insurance for family and corporate pension program
- Flexible mortgage discount, equal to 1/3 of the Central Bank key rate
- Free SberPrime+ subscription, discounts on products from partner companies
- Referral bonus for recommending friends to the Sber team.
