Engineering profile

Roles I'm a fit for

• Python Backend Engineer
• Full-Stack Engineer (Python + React / Next.js)
• AI / LLM Application Engineer

Open to remote roles, on a contractor basis through my individual entrepreneurship.

Core expertise

• Python backend

  APIs and services with FastAPI, Django and Flask; asynchronous programming; integrations between systems; PostgreSQL and Redis.

• AI systems

  RAG pipelines: document chunking, embeddings, vector search with pgvector or Milvus, relevance thresholds and answers with sources.

• Messaging and distributed systems

  RabbitMQ and Apache Kafka, background processing, event-driven architecture, retries and failure handling.

• Frontend

  React and Next.js with TypeScript: multi-page websites, interfaces for backends, client applications and admin panels.

• Automation and data

  Python automation, bots and API integrations; numerical processing with NumPy, SciPy and Pandas.

Selected technical projects

Each case explains the problem, the architecture and my role, with an interactive model of how the system behaves.

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  Case 01 · AI Engineering / Backend / Vector Search

  Intelligent Knowledge Base

  A RAG system for searching corporate knowledge

  The system answers natural-language questions from an organisation's internal documents: it finds relevant fragments with vector search and hands them to a language model as context.

  Python · FastAPI · PostgreSQL + pgvector · Milvus · Redis · LLM API

  Open the case
• 

  Case 05 · Distributed Systems / Backend Architecture

  Event-Driven Backend Architecture

  Asynchronous event processing and service-to-service communication

  Server-side architectures where components talk through message brokers and work is done asynchronously, by background handlers and event consumers, rather than inside one HTTP request.

  Python · FastAPI · RabbitMQ · Apache Kafka · Redis · PostgreSQL

  Open the case
• 

  Case 07 · Cloud / Database Engineering / Backend

  Cloud & Data Infrastructure

  Cloud infrastructure and data storage systems

  Working with cloud databases and storage: data structure, connecting backend applications, relational data, vector search and caching — each in the store that fits it.

  Python · PostgreSQL · Redis · pgvector · Milvus · Cloud databases

  Open the case
• 

  Case 06 · Full-Stack Development / Commercial Web

  Full-Stack Web Applications

  Complete web applications and websites built to order

  Client web solutions end to end: the interface, server-side logic, integrations and the database — a finished product rather than a standalone API.

  React · Next.js · Python · Django · FastAPI · Flask · PostgreSQL

  Open the case

Technical stack

• Backend

  • Python
  • Django
  • FastAPI
  • Flask
  • REST API
  • Asynchronous programming
  • Integrations between systems
  • Services and automation

• Frontend

  • JavaScript / TypeScript
  • React
  • Next.js
  • Multi-page websites
  • Interfaces for the backend
  • Client applications and admin panels

• AI / Data Engineering

  • RAG
  • LLM
  • Intelligent search
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Numerical methods and interpolation
  • Analytics and visualisation

• Databases

  • PostgreSQL
  • pgvector
  • Milvus
  • Redis
  • Cloud databases
  • Vector search

• Messaging and distributed systems

  • RabbitMQ
  • Apache Kafka
  • Redis
  • Asynchronous task processing
  • Event-driven architecture

• Networking

  • Python sockets
  • TCP
  • UDP
  • Client-server architecture
  • Corporate chats

• Automation and tools

  • Python automation
  • Bots
  • API integration
  • Docker
  • Cloud infrastructure
  • OpenAI Codex
  • Claude

• Also

  • C#
  • Object-oriented programming
  • Software design

Engineering approach

• Architecture first

  I start from the problem, the data and the load, and only then choose the tools.

• Failure is part of the design

  Error handling, timeouts and logging go in from the start, not after the first incident.

• Simplicity pays off

  Every abstraction has to earn its place. Separate services only when they solve a real problem.

• Code for the team

  Types, tests and a clear structure keep a project easy to change.

Professional links

• LinkedIn
• Telegram