High-Performance Engineering.
From
FinTech to Genetics.
20 years of experience in Enterprise systems (PayPal, Sage, Siemens). I deliver mathematical precision where scale, security and results matter — from billions of transactions to DNA analysis.
Complex Problem Solving
Delivering scalable solutions where standard approaches fail.
System Architecture
Mathematical Efficiency
My advantage doesn't come from knowing syntax, but from thinking.
As an engineer with mathematical background (I refined a proof of a combinatorial analysis theorem and developed an O(n) sorting algorithm for a specific data class), I view systems as sets of rules. At Sage Group, this allowed me to achieve 8x higher performance than the team average (16 tickets vs 2).
From "Thinking, Fast and Slow" to LLM Architecture
My fascination with cognitive psychology (Kahneman) allowed me to understand AI faster than others. I was already studying priming phenomena on early GPT-3 models.
// My early token experiment (based on Kahneman):
Context: "Kitchen/Food" -> AI completes "SO_P" as "SOUP".
Context: "Shower/Wash" -> AI completes "SO_P" as "SOAP".
I understand how semantic context changes token probability. This enables me to use Recursive Prompt Optimization — forcing the model to self-correct for deterministic results in medicine and finance.
Business Value
I don't "chat" with AI. I program it with context. This is crucial for building RAG agents that must be precise, not "hallucinate".
PayPal & FinTech
Billions of TransactionsChallenge: Migration and implementing changes in undocumented legacy code (Ruby/Microservices) at massive scale.
Engineering: Implementation of "percentage feature switch" for webhooks, enabling safe production testing.
Result: 0 downtime, organized architecture, full observability in Splunk.
Foot Locker
High Traffic LoadChallenge: Critical load during Black Friday, main site failure.
Engineering: Emergency refactoring of bottleneck algorithms, implementation of caching layers.
Result: Site restored within 2 hours, traffic handled without issues.
Legacy Rescue
Enterprise ScaleChallenge: Modernization of critical systems written in obsolete technologies.
Engineering: Gradual migration strategy, API abstraction layers, comprehensive testing.
Result: Savings equivalent to 30 FTEs (Telstra). ROI measured in millions.
My Mission
A leading medical center diagnosed my son with malignant cancer. Histopathology was based on microscopic imaging, and genetic verification was not standard procedure. I ordered private DNA testing, which unequivocally ruled out cancer and confirmed fibrous dysplasia.
This made me aware of a gap in the system: rare diseases can perfectly mimic other conditions ("diagnostic pitfalls"). Medical literature confirms that clinical presentation can be misleading and without molecular diagnostics, the risk of error is high.
At the current rate of medical knowledge growth, keeping track of the specifics of thousands of rare diseases is becoming humanly impossible. Doctors need engineering and AI support to navigate this data jungle flawlessly.
Goal 1: "Living Guidelines"
I'm building RAG systems that continuously analyze PubMed and update decision trees for doctors. Mission: deliver verified knowledge about rare variants to specialists in a fraction of a second.
Goal 2: Research & Drug Development
The app is just the beginning. I obtained a certificate in PCR techniques and bioinformatics from the University of Gdańsk to enter the scientific path. I don't just want to manage data — I want to understand the disease at its source (DNA) and ultimately launch drug research.
Contact
I respond personally. Usually within 24h.
- @ dm@humantechnology.com.pl
- Tel: +48 884 160 776
- Loc: Zielona Góra / Remote
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