Backend Engineer (Python, ML)

Sep 2024 – Mar 20261 yr 7 mos

• Designed and implemented a backend service for virtual flow metering, taking responsibility for its architecture and development.
• Implemented a layered architecture based on Clean Architecture principles, with FastAPI, asynchronous SQLAlchemy data access, PostgreSQL and ClickHouse storage, and Alembic migrations.
• Integrated physics-based well models and implemented calibration workflows using measured production data, enabling production rate reconstruction without direct metering.
• Developed ML models using ESP well data to predict five calibration coefficients for physics-based models and forecast production rates.
• Contributed to architecture improvements and refactoring across two shared libraries and the main service.
• Introduced coding standards, performed team code reviews, and developed tests and Sphinx documentation.

• Python
• FastAPI
• SQLAlchemy
• Alembic
• PostgreSQL
• ClickHouse
• scikit-learn
• XGBoost
• MLflow
• Docker
• Sphinx