Python backend engineering, applied AI systems, ML/NLP foundations, product delivery, and selected public launches.
Python Backend Engineer | Applied AI / LLM Systems Engineer | Product, Platform & Automation
I build backend platforms, AI-enabled products, and internal systems with practical foundations in mathematics, classical ML, NLP, and long-term maintainability, and I am open to freelance and contract delivery.
Commercial and project work covers backend platforms, AI assistants, knowledge systems, automation, bot products, parser and converter infrastructure, education workflows, and ML/NLP-heavy engineering tooling.
The through-line is pragmatic delivery: clarify scope, shape the system, implement it, validate it, document it, and leave the repository in a state that others can extend with confidence.
A structured offer for founders, operators, and teams that need a system shipped, stabilized, or integrated into real business workflows.
Russia and international projects, remote delivery, scoped contracts, rescue work, and turnkey builds.
Best fit for founders, teams, and operators who need a senior engineer to ship, stabilize, or extend a real system rather than stop at prototype-level output.
From marketing sites to internal services and production-facing digital surfaces for Russian and international markets.
Applied AI with concrete workflow value: assistants, agents, datasets, and business-hypothesis validation tied to actual operations.
For teams that need the runtime, metrics, and enterprise wiring to work, not just the application code.
Education products, public-facing packaging, and adjacent production services around technical delivery.
A practical mix of backend engineering, AI integration, classical ML, NLP understanding, infrastructure awareness, and full-cycle product work.
Python-first delivery for systems that need contracts, storage, queues, and operational sanity.
Combining LLM practice, classical machine learning, and NLP foundations into systems that work beyond demo conditions.
Clear boundaries between shipping velocity, product sense, and long-term maintainability.
Comfortable carrying the product across UI, APIs, background work, and release loops.
Selected projects across backend platforms, AI products, internal tools, and delivery infrastructure.
Unified files, documents, URLs, audio, video, and web sources behind one API with routing, OCR, transcription, rendering, storage, and structured outputs.
Handled roughly 20-25 source types, 10+ output formats, and 100+ conversion edges in one delivery surface.
Public summary only. Internal customer and NDA details are intentionally omitted.
Production-facing AI content workspace with structured LLM modes, background tasks, object storage, editor flows, and credit-aware usage logic.
Covered 5+ runtime services, 8+ core data models, and 9 workflow modes across writing, editing, and analysis.
Focused on usable workflows, async media handling, and operational cost awareness.
Built a role-aware AI SaaS for Telegram with billing tiers, webhooks, analytics, notifications, and separate bot runtimes per deployment context.
Included 20+ backend modules, 30+ frontend/admin pages, 12+ migrations, and configurable AI pipelines.
Strong example of product, backend, and operations ownership in one system.
Internal knowledge assistant with document ingestion, normalization, hybrid retrieval, reranking, answer assembly, and admin quality loops.
Supported 7+ document formats, multiple knowledge sources, provider fallback, and feedback-driven retrieval improvement.
Designed for reliability and iterative answer quality, not just a demo chatbot.
Open-source repository template with AGENTS routing, repo-local docs, validators, observability, browser proof loops, and machine-checkable release discipline.
Turns the OpenAI harness ideas into a public, reusable implementation that other engineers can inspect, fork, and adapt.
Published openly so the operating model is visible in code, docs, and verification artifacts rather than only described in prose.
Selected wins and technical results across hackathons, AI competitions, and reliability-focused tracks.
Participant, Personal and Trustworthy track
Worked directly in a benchmark-driven challenge environment focused on reliable and trustworthy autonomous agents.
Top-1 solution
Built a first-place pipeline by synthesizing training data with LLMs, training embeddings, and combining them with gradient boosting for psychotype detection and recommendation.
Finalist
Delivered an LLM-enabled resident-card and dynamic documentation workflow concept under hackathon pressure.
Winner
Built an interactive game platform using LLMs and image generation with a real backend pipeline.
Winner
Created an LLM-based story and test generation system that also received public coverage.
Commercial delivery, formal education, and parallel coursework in mathematics, ML, NLP, and MLOps developed side by side.
Commercial work across backend platforms, applied AI systems, automation, SaaS products, analytics, Telegram applications, and maintainable delivery workflows.
Built an AI mentor for education on top of LLM and retrieval workflows, while also helping connect technical implementation to customer value.
Formal study plus deep self-driven training in mathematics, software engineering, backend systems, classical machine learning, NLP, and model-focused engineering.
Studied programming, machine learning, NLP, and MLOps through MIPT Stepik tracks and multiple Open Data Science courses, then applied that material in real NLP and AI product work.
Selected links that explain the operating model behind the work and show how it was translated into public artifacts.
The OpenAI engineering post that framed the agent-first harness ideas later adapted into the public `trendon` template.
Open-source repository with AGENTS routing, repo-local docs, validators, observability, browser proof, and current-worktree publication.
A curated release deck for live public work: shipped code, learning assets, and links people can inspect right now.