---
title: "Roman Matveev"
url: "https://ramenm.com/en"
markdown_url: "https://ramenm.com/en/index.md"
locale: "en"
content_language: "en"
page_kind: "home"
source: "localized_path"
llms_url: "https://ramenm.com/llms.txt"
llms_full_url: "https://ramenm.com/llms-full.txt"
---

# Roman Matveev - Roman Matveev
> Backend Engineer (Python, integrations, applied AI) · Kazan, Russia · Remote · UTC+3

I build backend systems and integrations that still work after launch.

I take rough briefs and turn them into services: APIs, integrations, automation, and LLM features.

Open to remote or hybrid full-time roles, plus selective contract work on difficult integrations.

## Approach
I do my best work in messy environments: unstable external APIs, awkward workflows, and AI layers on top of real operations. The goal is to make delivery predictable and supportable.

### What I like building
- Python backends with clear API contracts
- Integrations and automation for unreliable external systems
- Applied AI features (LLM APIs, RAG, tool calling) that fit the workflow
- Observability, debugging, and support after launch

### Principles
- **Constraints first** - I start with external systems, data shape, operational risk, and support cost.
- **Simple systems last longer** - I prefer systems where data flow and failure boundaries are obvious.
- **Production is the real test** - A solution is ready when people can monitor it, debug it, and extend it safely.
- **Good delivery compounds** - Good backend work removes manual steps and makes the next release cheaper.

## Featured projects
Case studies told as problem -> decision -> outcome.

### [nnzen model catalog](https://ramenm.com/en/projects/llm-models-hub/index.md)
A live catalog with 500+ model cards that makes model research less scattered.
- Role: Founder / Backend Engineer
- Impact: Model choice went from tab-hopping to one place.
- Stack: Python, FastAPI, LLM APIs, RAG, Vector DB / pgvector, Tool calling

### [Custom Agent Core with MCP](https://ramenm.com/en/projects/enhanced-mcp-agent/index.md)
LLM core with a plugin execution layer for a developer assistant: hot reload, tool chains, and explicit context handoff.
- Role: AI Tooling Engineer
- Impact: The result is a reusable core that can support new workflows and tools without a full rewrite.
- Stack: Python, FastAPI, MCP, Tool calling, LLM APIs, Docker / Docker Compose, TypeScript

### [Trading Automation for an In-Game Marketplace](https://ramenm.com/en/projects/marketplace-trading-bot/index.md)
Automation for a constrained external marketplace with strategy logic, execution control, and logging.
- Role: Backend / Automation Engineer
- Impact: The system could keep running under platform changes instead of falling apart.
- Stack: Python, FastAPI, REST APIs / Webhooks, Reverse engineering, Docker / Docker Compose

### [Resilient Data Collection Tooling](https://ramenm.com/en/projects/resilient-data-collection/index.md)
Data extraction tooling for a changing web environment with persistent anti-bot friction.
- Role: R&D Data Collection Engineer
- Impact: Less firefighting, more predictable extraction.
- Stack: Python, Web scraping, Reverse engineering, Playwright, ClickHouse

## Experience
Experience is organized as problem, decision, and result: what was risky, what choice I made, and what changed after release.

### Freelance - Backend / Integration Engineer
- Period: Feb 2024 - Present
- Mode: Contract · delivery ownership
- Summary: Build backend, integration, and automation systems for real operating workflows across external APIs, process automation, and AI-assisted features.
  - Turn ambiguous requirements into maintainable services with explicit API contracts and support boundaries.
  - Design integrations for unreliable external systems with robust error handling, retries, logging, and observability.
  - Use reusable modules and integration patterns to shorten delivery time across new workflows.

### Independent projects / R&D - Independent R&D Engineer
- Period: Mar 2023 - Jan 2024
- Mode: Self-directed research
- Summary: Built focused backend and AI R&D between contracts, validating architecture patterns before using them in production-facing work.
  - Validated RAG and tool-calling patterns on working prototypes before client use.
  - Shifted from one-off scripts to reusable service components with clear interfaces.
  - Built a practical base that later fed applied AI and AI tooling projects.

### Bright Data - R&D Data Collection Engineer
- Period: May 2021 - Feb 2023
- Mode: Full-time
- Summary: Built and maintained data-collection tooling in a changing web environment: HTTP/JavaScript analysis, resilient request flows, and fast adaptation.
  - Analyzed unstable request chains and turned them into more resilient extraction logic.
  - Maintained extraction quality as anti-bot controls and page structure shifted.
  - Worked at the intersection of reverse engineering, reliability, and delivery speed.

## Have a messy brief? Let's turn it into a working system.
I'm a good fit when the brief is fuzzy, the systems are real, and the result has to survive production.

Remote or hybrid. Open to full-time roles and selective contract work.

### Contact links
- [Email](mailto:ramen44@yandex.ru)
- [Telegram](https://t.me/ramenm44)
- [VK](https://vk.ru/nyashpy)
- [GitHub](https://github.com/Ramenm)
- [nnzen.com](https://nnzen.com)

## Agent-friendly resources
- Markdown fallback for this page: https://ramenm.com/en/index.md
- llms.txt: https://ramenm.com/llms.txt
- llms-full.txt: https://ramenm.com/llms-full.txt
- Sitemap: https://ramenm.com/sitemap.xml