Alexander Sukhushin
RUEN
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AI R&D / PRODUCT ENGINEERING / DATA PRODUCTS

AlexanderSukhushin

AI and product engineer. From BI analytics to AI R&D: I build agents, platforms and data solutions that work and bring revenue.

6+YEARS IN IT / AI
6PROJECTS
9MATERIALS

How I am useful to your business

  • 01

    I amplify what is already strong in your business — with IT products and automation

    I do not plug holes and do not grab everything at once: I find the one strong thing that brings results and amplify it.

  • 02

    I bring AI where it is genuinely needed, if it is needed at all

    Not for a fashionable badge, but to strengthen the automation.

  • 03

    First step — an audit of your business processes

    I work only with what pays off and does not break what already works.

Public projects

Some of my most recent projects, where I took direct part as an engineer or a product lead.

All projects

How my stack evolved

The tools I picked up as I moved through the industry.

  1. 2018

    Power engineer

  2. 2020 — 2021

    BI analyst

  3. 2021 — 2024

    Lead data analyst

  4. 2024 — now

    Lead AI R&D specialist

Where my strength is

I cover the whole cycle of building an IT product or service: from research to production.

  • AI agents and LLM pipelines

    AI agents, RAG agents, answer quality evaluation, Agent Ops, LLM applications and deterministic LLM pipelines. I take every product to production and run its cloud infrastructure.

  • Solution architecture

    How it behaves under load, where the risks and vulnerabilities are. I count what development, growth and support will cost — before the start.

  • Data and metrics

    I always define the metric of success — the one I judge both the product and my own work by.

  • Shipping products

    I went the whole way: market research, testing hypotheses, building products, shipping them to production and the first sales.

How the work goes

The easiest way to show the approach is a live example: this site is built exactly by these principles.

  • 01

    Meaning first, code second

    I map the meaning before any layout.

  • 02

    Rules live in code

    The design system and other decisions that matter are enforced by hooks: you make a change without worrying that something breaks.

  • 03

    Every detail is taken to the end

    The code follows the industry quality standard and every component is strictly unified.

  • 04

    No illusions

    If AI does not apply to the task, or the effect will not pay for the work, I say so before the start.

  • What I love

    • Products where AI delivers a measurable effect, not a tick in a box

    • Data, automation and complex logic that takes a lot of thinking

    • Polishing every detail: from meaning down to the design of a button

  • What I take

    • IT products end to end: from idea and meaning to production

    • AI in business: AI agents, RAG, voice, automation of any complexity

    • An audit of your hypothesis: I build a prototype and an MVP fast, so you can see whether AI applies at all

  • What I don't take

    • "Make it like the competitor, just cheaper"

    • Grey schemes, gambling and fake engagement — at any price

    • Projects without an owner, where nobody makes decisions

My knowledge base

A free registry of what I know and have been through.

All materials

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A knowledge base on AI and engineering: how it all works, what the tools are and how the industry uses them.

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