Description
Project "Autonomous Management of Self-Service Device Lifecycle"
A comprehensive digital transformation of self-service device lifecycle management processes (ATMs, payment terminals, queue management systems, etc.) with the implementation of advanced AI technologies and intelligent automation. The project's goal is to maximize the automation of standard operations, reduce the workload on employees, and increase the efficiency of device maintenance.
Responsibilities
- lead the product direction of business requirements systems analysis when designing architecture and API (REST) for new services
- develop and maintain microservices on FastAPI and interact with related teams to integrate components and ML-models into services
- develop prompts and scenarios for generative models
- optimize service performance at all levels: code (asyncio, threading), data storage and queries (advanced SQL, indexes, sharding), caching (Ignite), work with queues (Kafka), creation and optimization of RAG-systems for working with data
- participate in designing test scenarios and creating test datasets. Control implementation quality, conduct deep defect analysis, and determine their root causes.
Requirements
- Python development experience from 2 years, including independent design of architecture for service components based on business requirements and the use of ML frameworks
- practical experience in creating high-load and fault-tolerant microservices on FastAPI, as well as products with implemented ML solutions;
- deep knowledge in the field of asynchronous applications, advanced SQL (window functions, query optimization), experience with message brokers (Kafka), Docker, Kubernetes
- good knowledge of algorithms and data structures
- understanding of the principles of generative models (GPT, GigaChat, etc.)
- high culture of teamwork using BitBucket/Jira, careful attitude to commits, knowledge of Agile processes (Scrum).
Will be a plus:
- experience in systems/business analysis
- knowledge of the basics of modern LLMs, AI-agent architecture, and experience in integrating ML-solutions (NLP, LLM) into products;
- skills in using AI-tools (GigaChat, DeepSeek, ChatGPT) for analysis, generation, and automation
- practical experience with ML/DS frameworks: (LangChain/GigaChain, Scikit-Learn, Pandas, etc.).
Conditions
- comfortable modern office at Kutuzovskaya metro station
- office work format during the adaptation period, then hybrid
- annual salary review, annual bonus
- corporate gym and relaxation areas
- more than 400 educational programs from SberUniversity for professional and career development
- extended voluntary health insurance, preferential insurance for family, and corporate pension program
- free SberPrime+ subscription, discounts on products from partner companies
- referral bonus for recommending friends to join the Sber team.
