Corporate University of Sberbank
Data Science
Sergey Plotnikov
AI Product Engineer / ML Developer
I build production AI systems end-to-end — from industrial data acquisition and machine learning to LLM services, backend infrastructure and deployment.
Engineering range, grounded in delivery
Engineering Stack
Machine Learning & Data
- Python
- pandas
- NumPy
- scikit-learn
- LightGBM
- CatBoost
LLM & AI
- LLM
- RAG
- RAGFlow
- OpenAI / LLM APIs
- Local LLM
- Prompt Engineering
Backend & API
- Flask
- FastAPI
- Django
- REST API
- HTTP
- Bearer Authentication
Data & Storage
- SQL
- ClickHouse
- MySQL
- SQLite
- Elasticsearch
- Redis
Education & Recognition
Lomonosov Moscow State University
Master’s Degree in Public Administration
Russian Presidential Academy of National Economy and Public Administration
Bachelor’s Degree in Management
National Award for Support of Talented Youth
Engineering Work
Industrial PLC Data Platform
Built read-only data acquisition pipelines for industrial equipment, moving PLC telemetry from edge collectors into ClickHouse for monitoring, analytics and downstream ML.
- Python
- Mitsubishi MC/SLMP
- PLC
- TCP/UDP
- SQLite
- ClickHouse
- Grafana
- FastAPI
- systemd
- Docker
Production ML & Shadow Inference for Manufacturing
Built ML pipelines on real manufacturing telemetry, from time-windowed PLC datasets and defect modeling to online inference and shadow-mode validation on production equipment.
- Python
- LightGBM
- XGBoost
- CatBoost
- scikit-learn
- ClickHouse
- Time Series
- Online Inference
- Shadow Inference
Industrial Signal Mapping & Machine Semantics
Mapped thousands of low-level PLC registers to real machine nodes and process semantics using historical telemetry, electrical documentation and field validation.
- Python
- PLC
- Mitsubishi
- ClickHouse
- YAML
- HMI
- Electrical Schematics
- RAGFlow
Technical Writing
Building an API to Control Glue Heads on a Machine. It Was... Complicated
Recently I got a very practical engineering task: build an API for several industrial glue units.
How to Build a Local AI Agent for Factory Documentation
In manufacturing, we have a typical factory problem: there is a lot of documentation, it comes in different formats, some of it is in Russian, some in English, some in Chinese, and you need to search through it quickly and preferably without mistakes.
Shadow Inference at the Factory: How to Test ML on a Real Machine Without Breaking Anything?
In my previous article, I wrote about how I cleaned PLC signals, removed leakage, separated real precursors from the usual machine modes, and generally tried to understand what exactly the model found.