Middle Python Developer (AI Agents) в UNIVERSITY OF BUSINESS AND SCIENCE Ташкент

Requirements

— 3–4 years of commercial experience in Python development.

— Strong knowledge of Python Core, OOP, and asynchronous programming (Asyncio, async/await).

— Hands-on experience with Django.

— Experience developing and integrating AI-powered applications and AI agents.

— Practical experience working with LLM APIs (OpenAI, Anthropic Claude, or similar).

— Understanding of prompt engineering and structured outputs.

— Experience with AI agent frameworks such as LangChain and LangGraph.

— Understanding of RAG (Retrieval-Augmented Generation) architecture and practical experience implementing it.

— Experience with vector databases.

— Knowledge of embeddings, semantic search, and document processing.

— Experience with function calling, tool calling, and integrating external APIs into AI agents.

— Strong knowledge of REST API development and third-party integrations.

— Experience with PostgreSQL, SQL, and ORM technologies.

— Experience with Redis and background task processing (Celery or similar).

— Knowledge of Git, Docker, and Linux environments.

— Experience writing unit and integration tests.

— Understanding of application security, error handling, logging, and performance optimization.

— Ability to independently develop, debug, and maintain backend and AI-powered solutions.

Will be an advantage:

— Experience developing AI agents for Finance, FinTech, or enterprise applications.

— Experience with multi-agent systems and agent orchestration.

— Knowledge of MCP (Model Context Protocol).

— Experience with LangSmith or similar LLM observability and evaluation tools.

— Experience with AI agent memory management and context handling.

— Knowledge of document parsing, OCR, and information extraction.

— Experience with microservices architecture.

— Familiarity with Kafka or RabbitMQ.

— Experience with cloud platforms and CI/CD pipelines.

Responsibilities:

— Design, develop, and maintain AI agents using Python.

— Integrate LLM providers and third-party AI services.

— Develop AI agent workflows, tool calling, and orchestration logic.

— Implement RAG pipelines for searching and retrieving information from documents and databases.

— Develop and maintain backend APIs for AI-powered applications.

— Integrate AI agents with internal systems, databases, and external services.

— Optimize AI agent performance, response quality, and processing time.

— Implement error handling, logging, testing, and monitoring.

— Work with the development team to deliver reliable and scalable AI solutions.

— Participate in code reviews and technical discussions.