Middle/Senior AI Agent Developer

Компания "Itransition"

Description:

We are looking for a skilled AI Agent Developer to join our AI team. You should have proven production experience with AI systems and a strong interest in learning and growing with modern agent technologies.

Responsibilities

• Develop, implement, and optimize AI agents capable of autonomous decision‑making, reasoning, and tool‑usage in real‑world scenarios.
• Build and maintain pipelines for agent planning, memory, retrieval, and multi‑step task execution.
• Integrate LLMs, vector stores, APIs, and external tools to enable complex agent behaviors.
• Design and refine prompting strategies, reasoning chains, and agent policies to improve reliability and accuracy.
• Implement evaluation frameworks for agent performance, safety, and robustness across diverse tasks.

• Monitor agent behavior in production, troubleshoot failures, and continuously improve quality of outputs.

• Optimize inference pipelines for speed, cost efficiency, and model/tool selection.

Requirements

• Strong Python skills and proven production experience with Agentic ecosystems

• Embeddings & Vector search
• Prompt & Context engineering
• RAG, CRAG, GraphRAG
• LangChain, LangGraph, CrewAI
• Agent's testing & verification
• Agentic Tools and skills
• AI-assisted coding models & tools
• Strong analytical and problem-solving mindset
• Ability to clearly communicate findings and trade-offs
• Ownership of tasks from research to implementation

• Curiosity and willingness to explore new approaches

• Level of English enough for efficient technical and business communication with native speakers

Nice to have

• Autonomous Agents & Deep Agents
• Model types and capabilities (Thinking, Multimodal models)
• LLMOps
• GenAI Observability
• Memory management & Prompt caching
• Model Governance & Data Governance (Responsible AI)
• Basics knowledge of Cloud infrastructure
• Agentic patterns
• Integration mechanics (MCP, AA-protocol)

• Basic understanding of machine learning fundamentals:

  • Supervised learning, models training, model evaluation

  • Overfitting, regularization, cross-validation
• Knowledge of statistical methods and probability theory

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