ML engineer building RAG, forecasting and full-stack apps
Screening 21,379 papers with a hybrid RAG pipeline. My M.Sc. thesis was a PRISMA 2020 meta-analysis of feature-extraction methods for EEG-based sleep staging, but the harder part was the screening. I built a hybrid RAG pipeline with an LLM referee layer for content filtering, MIDO-framework structured extraction and a two-stage quality check using the Newcastle-Ottawa Scale and PROBAST+AI, narrowing 21,379 records to a 140-paper methodological pool. Applied DerSimonian-Laird random-effects meta-analysis on logit-transformed F1 scores across W, N1, N2, N3 and REM stages, quantified between-study heterogeneity and mapped feature families to stage-level performance. Published at the 4th International Sarajevo Scientific Research and Innovation Congress, 2026.
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Manufacturing ERP platform with an ML layer. A cloud-hosted, multi-subdomain ERP running the day-to-day operations of a trailer manufacturer. I designed it and I am the only engineer on it: React frontend, FastAPI backend, PostgreSQL 16, nginx and Docker Compose on a VPS, plus a restorable triple-backup system. On top of that sit the models: demand forecasting, cost estimation and LLM-based document automation for quotations and technical documents, feeding daily production decisions.
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Swift & Fast iOS Apps with Precision 🏎️
Swift & Fast iOS Apps with Precision 🏎️
Crafting website templates & designs with Framer.
Crafting website templates & designs with Framer.
