Available for new projects
Marek Pizner — ML engineer & data scientist

AI that survives contact
with the real world.

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I help companies put AI and custom software to work — from a small tool that kills a manual routine, to production ML systems running on a factory floor. 9+ years of building the unglamorous parts that actually have to work.

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0+Years Experience
0B+Records Processed
0Products Live
0Companies Worked With
01 — Hire me

What I can help with

Most people find me somewhere between “we have an idea but no one to build it” and “this is broken and we’re out of ideas”. Both are fine starting points. Here is what working together buys you, and what it costs to find out.

🎯Start here if you’re not sure what you need

Strategy: working out what's actually worth building

The hardest part usually isn't the code — it's naming the real problem. We map how the work flows through your business today, find where time and money leak out, and decide what technology (if any) is worth pointing at it. You get a plan you could hand to anyone, not just me.

  • Sessions to map your current process end to end
  • Where AI genuinely helps — and where it's hype
  • A prioritised roadmap: quick wins first, big bets later
  • Build vs. buy, and which tools are worth paying for
  • Honest cost, effort and payback estimates
  • A written plan you own, with or without me
🤖

AI in your business

You know AI could help somewhere, but not where to start. I find the one workflow worth automating first, then build it.

  • Assistants that answer from your own documents
  • Agents that handle repetitive back-office work
  • Email, invoice & report processing
  • Honest advice on what AI won't fix
⚙️

Custom tools & automation

That spreadsheet everyone is scared to touch, the report someone rebuilds by hand every Monday — those become software.

  • Internal dashboards & admin tools
  • Scripts that replace manual routine
  • Integrations between systems that don't talk
  • Small web apps, built and handed over
📊

Data & ML engineering

The heavier end: pipelines that don't break at 3am and models that survive contact with production.

  • Forecasting, regression and predictive models
  • Data pipelines & warehouse setup
  • Deployment on Docker & Kubernetes
  • Image and video measurement tasks
🧭

Consulting & second opinions

Sometimes you don't need a build — you need someone who has shipped this before to tell you if the plan holds.

  • Architecture & code reviews
  • Technical due diligence
  • Getting a stalled project moving
  • Team mentoring on data & ML

How working together looks

three steps, no surprises
01

A free 30-minute call

Tell me the problem in plain language. No prep, no slides. If I'm not the right person, I'll say so and point you elsewhere.

02

Fixed scope and price

Within a few days you get a short written proposal: what gets built, what it costs, when it lands. No open-ended hourly billing.

03

Built, delivered, yours

Working software with the source, documentation, and a handover session. You own it — no lock-in to me or anyone else.

Not sure which of these you need? That’s exactly what the first call is for.

Book a free 30-min call →
02 — The work

Things I’ve built

Proof rather than promises. Paid engagements first — the systems running inside real companies — then the products I build on my own time, which is where you can actually click around and judge the craft yourself.

Client engagements

production systems, under NDA where it matters
Heavy industryU.S. Steel KošiceMachine Learning Engineer · Oct 2025–now

Two production systems on a working plant

A gas-blending optimizer for coke oven underfiring — pipelines unifying multiple plant databases and sensor feeds, regression models over high-frequency data, and a Django operator dashboard — holding heat targets while targeting GWh-scale annual energy savings. Alongside it, a real-time video pipeline whose UNet segmentation model sizes ore and dust on conveyor belts, replacing periodic manual sampling and flagging off-spec material as it moves.

PyTorchUNetDjangoKubernetesLabel Studio
Geospatial analyticsZoniqSenior Full-Stack Data Engineer · Sep 2019–Oct 2025

Where to put an EV charger, across three continents

Built and owned a location analytics platform helping charge point operators identify the top 10% of candidate sites across Europe, the US, and the Middle East & Africa. The core model predicts kWh throughput at a candidate site from AADT traffic counts and local demand signals; around it sit geospatial ingestion pipelines pulling millions of records from third-party APIs, partner databases and custom scrapers, and Kepler.gl maps and Power BI dashboards used for investment decisions.

PythonPostGISGeoPandasBigQueryKepler.glPower BI
RoboticsAdastraConsultant · Mar–Sep 2019

Behaviour systems for humanoid robots

Interactive behaviour for humanoid robots used in marketing and data collection: autonomous charging and movement, speech recognition and synthesis, and Google Sheets / SMS / email data sync.

PythonSpeech recognitionRobotics
Regulated financeNational Bank of SlovakiaSoftware Engineer · 2018

Backend for the ECB's AnaCredit

Led backend engineering for AnaCredit, the ECB's granular credit and credit-risk reporting system — secure ingestion and validation modules for regulated bank submissions, an internal admin UI and REST API, with monitoring, logging and retry strategies for high availability.

PythonSQLREST API

Products & side projects

built on my own time — all live, click any of them
03 — Who I am

The person you’d be hiring

The CV part, for anyone who wants it: where I’ve worked, what I actually know how to use, and what I do when I’m not at a keyboard.

Currently a Machine Learning Engineer at U.S. Steel Košice, building process-optimization and computer vision systems for heavy industry — a gas-blending optimizer for coke oven underfiring, and a real-time segmentation model that sizes ore and dust on conveyor belts. I own both end to end, from annotation and training to Kubernetes deployment.


Before that, six years as a Senior Full-Stack Data Engineer at Zoniq, building a geospatial analytics platform for EV charging networks — the model predicting kWh throughput at candidate sites, the pipelines feeding it millions of records, and the maps and dashboards clients made investment decisions on. Earlier still, regulatory backend systems for the ECB’s AnaCredit at the National Bank of Slovakia.


MSc in Intelligent Software Systems from STU Bratislava (thesis: community detection in networks), after a BSc in Computer Science at the same faculty. Based in Prešov. Outside work: weightlifting, running, books, and building finance tools that make data accessible to everyone.


I take on a small number of freelance projects alongside that — which means I’m selective, but the ones I take get proper attention.

Where I’ve worked, and what the work was made of

Technical arsenal

what I reach for, roughly in order of how often
Machine learning
PyTorchScikit-learnImage segmentation (UNet)RegressionDemand forecastingMLOps
Data
PythonSQLPandasDuckDBBigQueryPostgreSQL / PostGISGeoPandasDVCPapermill
Cloud & infrastructure
AWS (S3, Glue, Athena, Lambda)Google CloudDockerKubernetes
Applications & visualisation
DjangoPower BIKepler.glJupyterLabel StudioClaude Code
Currently taking on projects

Got something that needs building?

Book a free 30-minute call and describe the problem in your own words. You’ll leave with a straight answer on whether it’s worth doing, roughly what it takes, and whether I’m the right person for it. No pitch deck, no obligation.

Remote worldwide · English & Slovak · Usually reply within a day