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Grid Dynamics приглашает опытного инженера ПО для разработки автономных агентных AI рабочих процессов и мульти-агентных систем, которые автоматизируют сложные процессы разработки. Вы будете работать над backend на Python, LangGraph и LangChain, обеспечивать наблюдаемость через LangSmith и использовать GenAI-инструменты.
Кандидат должен иметь 3+ года опыта, знание LangGraph/LangChain, опыт промпт-инжиниринга и Docker, плюс англоязычное общение.
Grid Dynamics provides technology consulting, platform and product engineering, AI, and advanced analytics services. The company helps enterprise organizations solve complex technical challenges and achieve business outcomes during digital transformation.
Architect, design, and deploy autonomous agentic AI workflows and multi-agent systems that automate complex, end-to-end software development lifecycle processes; Develop scalable back-end services and orchestrations using Python, LangGraph, and LangChain for multi-agent communication, state management, and task planning; Implement observability, evaluation, and tracing frameworks with LangSmith to monitor agent behavior, optimize performance, and ensure output reliability; Apply prompt engineering techniques and GenAI development tools such as Claude Code to build code generation and refactoring pipelines; Manage containerized runtimes with Docker and write complex Bash/shell scripts for environment execution and sandboxing; Collaborate with cross-functional engineering teams to evaluate generated code across Java, .NET, and Angular technology stacks; Address complex technical challenges, navigate ambiguity, and maintain high standards for generated code quality and security.
3+ Years of commercial software engineering experience and a proven track record of developing production-grade Python applications; Hands-on experience developing agentic AI systems, multi-agent architectures, or advanced Retrieval-Augmented Generation frameworks; Strong proficiency with LangGraph and LangChain for building complex, stateful AI agent workflows; Practical experience with LangSmith or equivalent LLMOps platforms for observability, tracing, debugging, and evaluation of LLM pipelines; Practical experience using modern GenAI coding acceleration tools such as Claude Code, Cursor, or GitHub Copilot in daily software development; Expertise in prompt engineering, LLM orchestration, and dynamic context management; Solid hands-on proficiency with Linux, Bash/shell scripting, and Docker containerization; Conceptual understanding or basic hands-on familiarity with Java using Maven/Gradle, .NET, and Angular; Highly adaptive mindset, strong problem-solving skills, and excellent written and verbal English communication skills.