I am an ML/Python engineer with hands-on experience in MLOps/DevOps, data preparation, FastAPI services, and reproducible experimental workflows. I build practical data and machine learning systems for industrial and operational use cases.
I develop Python workflows for LLM automation, information extraction, and structured processing of unstructured data. My work includes dataset preparation, preprocessing, model-serving-oriented services, and automation that improves reproducibility and reduces manual effort.
I have experience designing event-driven simulation and decision-support systems. I work with asynchronous architectures, event buses, state storage, JSONL logging, scenario execution, random-seed isolation, and human-in-the-loop approval workflows.
I build backend services using FastAPI, WebSockets, PostgreSQL, TimescaleDB, MongoDB, and SQLAlchemy. I am comfortable containerizing ML and data environments with Docker, managing Linux-based infrastructure, and maintaining reliable API and telemetry pipelines.
Alongside engineering, I bring substantial financial analytics and planning experience from industrial and logistics environments. I analyze cash flows, liquidity, banking data, intercompany settlements, KPIs, and management reporting.
My research interests include multi-agent reinforcement learning, constrained decision-making, maintenance optimization, industrial diagnostics, and cyber-physical AI systems. I have published work on hierarchical multi-agent reinforcement learning and Dempster-Shafer-based fault diagnosis.
