While other candidates refresh job boards and miss windows, my n8n+AI pipeline sees a vacancy 4–8 minutes after it's posted, filters it, drafts the application, and pings me on Telegram.
You're reading this because the system worked.
I prototype with Claude Code & Cursor and harden with 15 years of engineering. Frontend, backend, infrastructure — idea to production in days, not sprints.
Agentic systems that do real work — not chatbots. An executor performs, a reviewer scores, the loop runs until ≥90% quality. RAG, MCP, evals, fallbacks.
Self-hosted n8n on a VPS I provision, deploy and harden myself. Workflows that run 24/7, integrate 50+ services, and replace 90% of manual ops.
A live system I built and run for myself. It's also the proof that what I describe in pillar 02–03 actually works in production. Here's the entire pipeline.
The work I keep on top of my GitHub — AI products, full-stack apps and the platform code behind them. Live from @SergeMiro.
A TypeScript facade layer — a clean, typed integration interface that hides third-party complexity behind one API.
Real-time telephony API & analytics platform built at Fimainfo — call routing, live stats and SLA monitoring.
Online shopping app — a full e-commerce front end with cart, checkout and product catalogue in React / TypeScript.
Web app for an automotive dealer — vehicle catalogue, listings and customer-facing UI in React / TypeScript.
AI solutions for EU business — LLM-powered workflows that automate multilingual compliance and back-office paperwork.
My portfolio & AI avatar — a full-stack site with an embedded LLM chat trained on my CV and projects.
Multi-agent back-office for FR call-centre campaigns. Executor works, reviewer grades, loop runs until score ≥ 90%. 30+ hrs/week saved per team. Yes, including URSSAF.
Inbox → structured JSON. Executor extracts, reviewer checks, n8n routes. Eats messy emails for breakfast.
Stats agents collect, security agents sniff, dashboards complain. Multi-tenant, SLA-aware, sleep-friendly.
Multi-exchange bot — signals, risk-mgmt, back-tests. Personal lab. Not financial advice (please).
If you're hiring for a senior engineering role with AI, automation, or platform components — these are the patterns I'd bring to your team on day one.
The pipeline above wasn't a ticket — I saw a workflow problem, designed end-to-end, shipped. Same approach to product work.
"Production over prototype" isn't a slogan — it's how I scope.
Shipping LLM features since GPT-3.5. I know where AI breaks (eval, hallucination, cost, latency) and how to ship around it. Claude is one piece — deterministic code catches its mistakes.
"Senior" with AI means knowing when not to use it.
VPS, Docker, n8n, Postgres, Firecrawl, Telegram bots — self-hosted and hardened (OWASP, firewalls, secrets), on hardware I pay for. I'd rather understand a system than rent it.
If I can't run it locally, I don't trust it in prod.
Dijon, FR — open to remote / hybrid across FR & EU. Specs, reviews, customer copy in both.
Fluent FR · senior-level EN · native UA/RU. C++ in CSS.
Groundbreaking papers, essays, and moments in AI, programming & systems — each card opens the whole article. Swipe / drag the stack.
Opinionated. Self-hosted by default. Boring where it needs to be, sharp where it matters.
This is the system I run every day: ai-agents-config — five agent profiles, each adapted to three harnesses (Claude Code, Codex, OpenCode), fronted by Manager — my Hermes Agent AI — on my own VPS. Pick a team, hand it a real task, read the answer right here. No sign-up, no sales call.
The wire between me and every team. Give it a task — it tells you who it would wake up and why.
Real model, no tools — it explains what it would do. My real agents run on my VPS with file, shell and DB access.
I'm open to AI engineering and senior full-stack roles — fully remote across EU, or hybrid in France. The CV is one click away — though honestly, this page is most of it.