ml engineer for autonomous agents

Описания нет

• Design, develop, and optimize autonomous AI agents, orchestrators, and multi-agent solutions using LangChain and LangGraph
• Design and implement LLM interaction logic, including prompts, instructions, and context management
• Build and maintain robust, scalable backend infrastructure using Python and modern software design patterns
• Implement agent state management and design complex workflows involving multiple agents and tools
• Connect AI agents to data sources and services, including REST APIs, microservices, databases, and corporate systems
• Design and implement event-driven architectures, ensuring observability and interoperability between system components
• Integrate generative AI with deterministic logic, business rules, and predefined workflows to create hybrid solutions
• Apply software engineering best practices, including Git, CI/CD, automated testing, and agile methodologies
• Deploy and manage applications in cloud environments using Docker and Kubernetes
• Apply the MCP (Model Context Protocol) for model communication and orchestration

• At least 4 years of software development experience, with significant focus on AI and/or backend development
• Strong practical Python development skills
• Demonstrable experience with LangChain and LangGraph, including agent development, tool definition, workflow orchestration, and advanced state management
• Deep understanding and application of software design patterns, including Hexagonal Architecture, Domain-Driven Design (DDD), and modular, maintainable design principles
• Experience integrating LLMs and designing interaction logic, prompts, and instructions
• Knowledge of microservices architectures and experience with Docker, Kubernetes, and cloud deployments
• Experience with Git, CI/CD, automated unit, integration, and E2E testing, and a strong commitment to code quality
• Understanding of fundamental AI concepts, including generative AI, machine learning, and automated reasoning
• Будет плюсом: MCP (Model Context Protocol), event-driven architectures and distributed systems, observability tools (logging, monitoring, tracing), open-source contributions related to AI or software development, database design and optimization

• Opportunity to work on innovative, high-impact AI projects
• Dynamic, collaborative work environment focused on continuous learning