SUMMARY
Process & Manufacturing Engineer (Aerospace) · Applied AI/ML — Modelling, Verification & Validation
Aerospace process engineer at Airbus Defence and Space with an independent applied-AI/ML research track centred on verification and validation. Three years developing technological processes, assembly sequences and control documentation for prototype and serial aircraft production; in parallel, building probabilistic models and reproducible validation pipelines — deflated performance metrics, combinatorial purged cross-validation, calibration and uncertainty quantification — and publishing the results (public preprint; Zenodo DOI). FEM-certified; CATIA V5, SAP ERP and Python in daily use.
EXPERIENCE
PROCESS ENGINEER
Airbus Defence and Space
Warsaw, Poland
- —Progression: Intern (Sep 2023) → Junior Process Engineer (Jan 2024) → Process Engineer (May 2026)
- —Develop technological processes, assembly sequences and control cards for parts and subassemblies in prototype and serial aircraft production (Airbus Poland and CASA/Airbus cooperative programmes)
- —Optimised production workflows and material/fastener allocation — up to 30% efficiency improvement and ~€50K annual cost savings
- —Verify processes against new technical developments, inspection-monitor workstation compliance, and implement design-office changes
- —Define tooling, fixture and metrology assumptions; select tools on techno-economic criteria; normalise working time
- —Deliver internal training on design & technological documentation; apply Lean standardisation and FOD discipline daily (CATIA V5, SAP ERP, GD&T)
CO-FOUNDER & CTO
K&S Venture Group — Clashpoint.me
Warsaw, Poland
- —Designed, built and operate an AI-driven sales-coaching platform end to end — FastAPI, PostgreSQL, Next.js/React, LLM features
RESEARCH & PUBLICATIONS
Public preprint · 2026
Deflated performance metrics, PBO/CSCV, combinatorial purged cross-validation and walk-forward to quantify the out-of-sample reliability of published forecasts — fully reproducible, pre-registered pipeline.
Open source · Zenodo DOI 10.5281/zenodo.20645678
Multi-agent pipeline enforcing pre-registration and reproducibility across the research lifecycle; centrepiece pre-registered gate-calibration study with honest reporting of threshold failures.
Solo research codebase · ≈274K LOC
A single frozen, CSCV-validated momentum strategy (cfg_00 / MetaEnsemble v56) over ~15 years of hourly US-equity data — PBO 0.0108, Sharpe 5.24 out-of-sample (through COVID), 424 live-tradable universe. Pre-registered validation with an honest validated-vs-traded distinction and transparent reporting of failure modes.
Manuscript in preparation
NumPyro hierarchical Bayesian classifier with a LightGBM baseline for near-Earth-object triage; calibration, uncertainty quantification and a reproducible training pipeline.
SKILLS
VERIFICATION & VALIDATION
AI / ML
MODELLING & SIMULATION
MANUFACTURING ENGINEERING
BACKEND & PROGRAMMING
EDUCATION
MSC PROJECT & PROCESS MANAGEMENT
Collegium Civitas
Warsaw
SOFTWARE ENGINEERING — 42 WARSAW
Peer-to-peer coding program
Warsaw
- —Project-based, peer-to-peer software engineering — C, algorithms, Unix systems
BENG MECHANICAL ENGINEERING
Silesian University of Technology
Gliwice
- —Thesis: feed mechanism of mining rigs and bolting machines · Major GPA 4.17 / 5.00
CERTIFICATIONS
LANGUAGES & ELIGIBILITY
Polish (native) · English (professional working proficiency — daily working language at Airbus). Holder of a Polish national personnel security clearance; experienced with export-control and classified-information regimes.