RadAI-1984

AI ORCHESTRATOR · FULL-STACK · DATA SCIENCE · ML

Founder's idea → working MVP. Fast. Autonomous. No hand-holding.

I'm Andrii Radkovskyi — AI Orchestrator. I direct AI agents across the full stack: product development, data science, ML, deployment. One person doing what used to take a team. Twenty years in nuclear power — where getting it wrong isn't an option. Same discipline, now in software. Already shipping.

20 years high-stakes engineering Full cycle: dev → DS → ML → deploy AI agents as the production force
01 / 06

The exact match

You described the person you are looking for — fast with a product, autonomous in technical decisions, AI as the key parameter, delivers to a working result, no micromanagement. Here is that description, set against the evidence.

AI Orchestrator — full-stack, fast to a working MVP

Built an electricity forecasting product end to end — solo. Requirements, data pipeline, ML ensemble, full-stack app, DevOps. One month to a working MVP. Not with a team — with AI agents I directed. That's the method.

Actively uses AI — the key parameter

For me AI agents are not a side helper — they are the main production force, on every phase. The product is the proof, not the claim.

Covers data science — not just the app layer

I don't just build the app on top. I go from raw data to a production model — feature engineering, model selection, ensemble, validation on real data. You know what that pipeline takes. You know it's usually a team. I do it solo.

Senior-level ownership — no micromanagement

You wrote "senior-level ownership". I have twenty years of it — on the operational line of the national nuclear operator, deciding in real time and answering for the outcome. Ownership is not a title I take; it is a reflex I trained.

02 / 06

The strongest project — in production, earning daily

1moidea to working MVP
10–20%accuracy gain over human expert
1–2MkWh saved per month
1engineer, full cycle

Hourly electricity-consumption forecasting for a regional power company — in production, earning money every day. The hard constraint: forecast for tomorrow ready by noon, with only yesterday's actuals as input. Engineered to be accurate inside that real-world limit.

What this usually takes: a data science team + a dev team — two separate teams. I built it alone: full stack, ML ensemble of 4 models, 37 engineered features, 5 levels of testing including data-leakage checks.
How it was built: AI agents as the production force, under my direction. Not "AI helped here and there" — that is the working method, and this product is the proof it ships.
LightGBM + CatBoost TimesFM neural net 37 engineered features FastAPI / Python 3.11 React 18 / TypeScript PostgreSQL 16 Docker · VPS
13 more working products — energy, finance, security, agriculture Open full portfolio →
03 / 06

The method

One method. Every project. Three stages — and what matters is what each one actually holds.

1

Understand the task

Not the brief as written — what the founder actually needs. Most engineering failures start here. I stay until I understand the real problem, not the described one.

2

Design and build

Architecture before code. MVP before full product. Every iteration tested against reality, not the spec.

3

Ship and own it

Fully agentic build — AI agents through code, tests, DevOps. The product ships, stays running, and I stay accountable.

A prototype instead of a specification

A working product — even a single file — beats a stack of documents. It gets tested in reality at once.

Design from the failure scenario first

Before the happy path, the worst case. Twenty years of investigating accidents taught me this is engineering, not pessimism.

04 / 06

The discipline and the pace

Twenty years in nuclear power — the whole path, from the ground up to the top of the operational line. Then, at Energoatom — the national operator of every power unit in Ukraine — senior analyst for the investigation of emergency situations across all of the country's nuclear plants: root-cause analysis, finding hidden defects before they become events.

That is the discipline of a field where a mistake is not allowed to exist. It is exactly what I bring into software — and it is rare in this industry.

I came from that world, made one decision, and went all in. The result: a working ML product, a domain I'd never touched, full cycle, solo. That's the pace I move at. Senior-level ownership isn't about years in software — it's about who makes the call under pressure and owns what happens next. That's a reflex, not a claim.

05 / 06

The proposal — zero risk for you

I don't know your startup yet. That is not a gap — it is the point. Understanding what the founder actually needs, beneath the words, is the first and most important skill. Everything after it is execution.

YOU GIVE

One real task. One week. Freedom to act — no micromanagement.

YOU GET, IN A WEEK

A deep reading of the task and a solution architecture — most likely an MVP architecture as well. A concrete result you can judge.

You risk one week. In return you see exactly who you are dealing with — before any commitment is made.

06 / 06

Let's talk

If the person you described is the person on this page — here is how to reach me.