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spqr-86/README.md

Hi, I'm Petr 👋

AI Engineer | LLM Systems · RAG · Multi-Agent | Document Intelligence

🚀 About Me

🔭 Building RAG pipelines and multi-agent systems for complex technical documents

🎯 Focus: production eval-driven development — 200+ test cases, LLM-as-judge, Ragas metrics per release

🏗️ Architecture I work with: Router → RAG Agent (ReAct, LangGraph) → Verifier

📫 Reach me: [email protected] | Telegram: @PetrBaldaev

💼 Open to AI Engineer roles — Remote / Moscow / International

🛠️ Tech Stack

LLM Systems

Python LangGraph LangChain OpenAI Hugging Face

Vector & Search

Qdrant ChromaDB BM25

Backend & Cloud

FastAPI Docker Google Cloud PostgreSQL

🔥 Featured Projects

Regulatory Compliance Q&A

Production RAG over Russian regulatory documents (GOST, SNiP, Labour Code). Result: 7.7/10 correctness · 93.6% faithfulness · 100% out-of-scope abstain · $0.01/query · 9.5s mean latency

Regulatory MAS Agent

Multi-agent system (LangGraph + MCP) — Coordinator, RegulationsAgent, WebAgent, CriticAgent with self-revision loop.

Financial Budget Assistant

Multi-agent expense tracking via Telegram. Google ADK · MCP · Google Sheets API · Cloud Run CI/CD.

📊 Numbers that matter

Project Key Metric Impact
Regulatory RAG 93.6% faithfulness, 100% abstain on OOS 12× faster search
Water Treatment Analyzer 74% gap-analysis accuracy (domain validated) −40% manual review
Regulatory MAS Self-revision loop · 4 specialized agents MCP tool integration

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  1. regulatory-rag regulatory-rag Public

    Production RAG for Russian regulatory docs (GOST, SNiP, Labour Code) — 7.7/10 correctness, 0.936 faithfulness, 100% out-of-scope abstain, $0.01/query

    Python

  2. Financial-Advisor-Multi-Agent-Budget-Assistant-v2 Financial-Advisor-Multi-Agent-Budget-Assistant-v2 Public

    AI-powered family budget management through specialized agent collaboration and Model Context Protocol integration

    Python

  3. gitlab-onboarding-rag gitlab-onboarding-rag Public

    A multilingual (RU/EN) RAG system built on 847 pages of GitLab documentation to provide instant answers for new employees, achieving 89% response accuracy and sub-3-second latency.

    HTML

  4. regulatory-mas-agent regulatory-mas-agent Public

    Multi-agent system (LangGraph + MCP) for Russian regulatory Q&A — Coordinator, RegulationsAgent, WebAgent, CriticAgent with self-revision loop

  5. titanic-booking-ai titanic-booking-ai Public

    A conversational AI agent simulating a 1912 Titanic booking clerk, maintaining historical accuracy (94% factual precision) and character consistency through advanced prompt engineering.

    Python