Position: Machine Learning Engineer
Category: Python
English level: upper
Years of experience: 7

- Audio Chord Estimation model achieves SOTA result (but less number parameters)
- Reduced resource usage: trained models on TPUs and edge devices such Nvidia Jetson costed less than rent GPU
- Data pipelines on lambdas that reduced resource usage and cost.
- Top pandas contributor. My code is executed on ~15M devices monthly according to PyPI Stats.
- Core developer of system that performs > 20M requests per day.
- Developed reliable processing System that was handling more than $3M per month.
- Built the model for audio chord estimation using CNN and LSTM (pytorch, TPU, tensorboard).
- DSP, data collection and preprocessing (python, scipy, pandas, AWS)
- System Design and backend development: Python (async), Postgres, ELK, Redis, Kubernetes, AWS, Pub/Sub
- Core developer in British startup (over 1M active users): improved core system, developed a bunch of features, significantly reduced resource usage, improved stability of ecosystem (async python, ELK, Postgres
- Top US insurance company: set up ETL pipeline, built several services (python, flask, aws, pandas, redshift)
- FinTech startup: design and implementation the payment processing system, e-currency exchanger, BTC-currency exchanger