Елена

With 4 years of experience as an AI/ML Engineer and full stack developer. I specialize in Natural Language Processing and Predictive Modeling. I leverage tools like Langchain, LangGraph, Hugging Face, and OpenAI API and vector databases to architect and optimize RAG and document processing pipelines, to design and deploy advanced agentic GenAI solutions, ensuring ethical AI use.

I am proficient in the full machine learning lifecycle, from model development using PyTorch, to optimizing and deploying models for production. I also have expertise in MLOps using DVC, as well as backend development with FastAPI and SQLAlchemy. Additionally, I am well-versed in cloud platforms (Azure, AWS, OCI) and DevOps practices, including Docker, Kubernetes, and GitHub Actions.

Throughout my career, I've delivered impactful solutions across diverse domains, including Automation, Healthcare, Marketing, Transport, and Finance. Have masters degree in artificial intelligence.

AI ENGINEER at Intelligent Bank (2024-10 – Present)

Conducted exploratory data analysis and feature engineering for customer spending patterns, credit risk trends analysis, with deployment and monitoring of ML models.

• Conducted exploratory data analysis (ydata_profiling, Matplotlib, Plotly, statsmodels) to detect customer spending patterns, credit risk trends, and anomalies in transaction data
• Engineered features such as lagged payment behavior, rolling sums over 7/30 days, and calendar-based indicators (e.g., weekends, holidays)
• Created a statistical outlier detection layer that improved ML model robustness and uncovered unusual transaction patterns

Data scientist / ML ENGINEER at IBA (2021-09 – 2022-11)

Developed forecasting models for revenue prediction using ensemble methods combining time-series and machine learning approaches.

• Conducted exploratory data analysis (ydata_profiling, Matplotlib, Plotly, statsmodels) to identify trend, seasonality and anomalies in historical revenue data
• Engineered new features from existing data, such as lagged variables, rolling averages and calendar-based features (e.g., holidays, day of weeks)
• Created a statistical outlier detection layer that improved overall ML model performance and delivered various insights
• Developed and implemented an ensemble forecasting model combining time-series methods (e.g., SARIMA, Prophet) with machine learning models (e.g., Logistic Regression, XGBoost)
• Designed interactive visualizations using Plotly to present data patterns and forecasting result to non-technical stakeholders
• Implement interpretability layers to enhance transparency and explainability of model predictions
• Deployment, performance monitoring and maintenance of forecasting pipeline
• Code review

Patient health intelligent assistant | AI/ML ENGINEER (2022-11 – 2024-10)

Developed ensemble forecasting models for early warning of patient deterioration using time-series and machine learning methods.

• Conducted exploratory data analysis (ydata_profiling, Matplotlib, Plotly, statsmodels) to identify trends, seasonality, and anomalies in patient vital signs (blood pressure, pulse, temperature, lab results)
• Engineered features including rolling averages over 3/7 days, lagged measurements, and calendar-based features (e.g., day of week effects)
• Created a statistical outlier detection layer that filtered measurement artifacts and improved clinical ML model performance
• Developed an ensemble forecasting model combining time-series methods (SARIMA, Prophet) with ML models (Logistic Regression, XGBoost) for early warning of patient deterioration
• Designed interactive Plotly visualizations to present patient risk trends to medical staff
• Implemented interpretability layers to explain why a patient was flagged as high-risk
• Managed deployment, performance monitoring, and maintenance of the prediction pipeline
• Performed code reviews

Business assistant | AI/ML ENGINEER (2022-11 – 2024-10)

Developed forecasting models for business revenue and demand prediction using ensemble methods.

• Conducted exploratory data analysis (ydata_profiling, Matplotlib, Plotly, statsmodels) to identify trends, seasonality, and anomalies in business revenue and demand data
• Engineered new features including lagged variables, rolling averages, and calendar-based features (e.g., holidays, day of week)
• Created a statistical outlier detection layer that improved overall ML model performance and delivered actionable business insights
• Developed and implemented an ensemble forecasting model combining time-series methods (e.g., SARIMA, Prophet) with machine learning models (e.g., Logistic Regression, XGBoost)
• Designed interactive visualizations using Plotly to present data patterns and forecast results to non-technical stakeholders
• Implemented interpretability layers to enhance transparency and explainability of model predictions
• Managed deployment, performance monitoring, and maintenance of the forecasting pipeline
• Performed code reviews

Master of Artificial Intelligence in Artificial Intelligence – Belarusian State University of Informatics and Radioelectronics (2023-01 – 2026-01)

Bachelor of Electronic System Engineering in Electronic System Engineering – Gomel State University (2019-01 – 2023-01)