Senior Gen AI/Agentic AI Engineer

Навыки

• AI-агенты
• AI-инструменты в работе
• API (интеграции)
• AWS
• Azure
• Отладка и поиск ошибок
• Дообучение моделей

О компании и продукте

• Senior Gen AI/Agentic AI Engineer (#4512535101) - Office/Remote | N-iX. Search Job. Senior Gen AI/Agentic AI Engineer. Senior Gen AI/Agentic AI Engineer (#5698). Work type. Office/Remote. Technical Level. Job Category

Требования

• Practical experience developing and evaluating GenAI services, including RAG systems, understanding of the "ReAct" (Reasoning + Acting) paradigm and Agentic RAG, LLM API integration, and prompt/context engineering, as well as training, fine-tuning, and deploying ML models in production environments
• Knowledge of ML/GenAI frameworks: LangGraph or LangChain, PyTorch / TensorFlow, Hugging Face, OpenAI/Anthropic SDK
• Practical commercial experience working with AI models via API (Gemini, Anthropic) and self-hosted models (Llama 3, Mistral, Mixtral), with an understanding of model differences based on functional/non-functional requirements (FR/NFR)
• Deep understanding of how LLMs interact with external APIs via Function Calling
• Experience with vector databases, semantic search methods, and principles of database structuring and cleaning
• Practical experience with at least one cloud platform (AWS, GCP, or Azure)
• Proficiency in Python and understanding of asynchronous programming
• Understanding of the AI model lifecycle: monitoring, versioning, and quality evaluation (RAGAS, DeepEval), with hands-on experience using these tools
• Experience with Guardrails: setting hard constraints on conversation topics and agent actions, filters that automatically strip personal data before sending requests to external LLMs, and the ability to build output filters that fact-check generated responses before they're displayed
• Deterministic Logic Integration — running AI agents on strict schemas to prevent the model from "making things up."
• A plus: knowledge of automated testing approaches for evaluating responses across large datasets to measure hallucination rates before MVP launch
• Knowledge of Human-in-the-loop mechanisms, ensuring agents cannot execute actions without final user verification
• Ability to design memory systems that store context from a client's previous conversations and operations for personalization (Long-term Memory & User Context)
• Ability to clearly communicate complex technical concepts and mentor team members
• Ability to quickly test and evaluate new libraries and approaches
• Analytical problem solving - debugging complex "black boxes" and understanding why an agent behaves unpredictably
• Focus on delivering a working prototype rather than a perfect research paper
• Close collaboration with Frontend and Backend developers to seamlessly integrate AI agents into the required environment
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