About:
Yauheni Suvitau is a highly skilled machine learning and NLP engineer with more than eight years of experience delivering production-ready AI systems across healthcare, fintech, telecommunications, and ecommerce. His strong mathematical background and practical engineering approach enable him to create intelligent solutions that turn raw data into actionable insights and scalable products.
Proficient in Python, PyTorch, SQL, and modern ML frameworks such as CatBoost, XGBoost, LightGBM, and Sklearn, Yauheni has developed and deployed models for classification, regression, forecasting, named entity extraction, and computer vision.
His background also includes designing data pipelines, improving algorithms, and integrating ML services into complex system architectures. His cloud experience includes AWS (EC2, S3, SQS) and Azure, supported by strong DevOps skills in Docker and Kubernetes for containerization and scalable deployment.
Throughout his career, Yauheni has built AI-driven platforms that delivered measurable business impact, including:
- A healthcare chatbot that parses medical literature and uses retrieval-augmented generation to respond in a doctor's voice
- A computer vision system for real-time operating room analytics using YOLOv8 and multimodal data streams
- Financial sentiment analysis and NER models that classify news and extract insights from market data
- A digital mortgage platform powered by fast, high-accuracy document processing microservices
- Churn prediction and fraud detection systems for telecom providers, fully integrated into real-time decision engines.
His experience covers advanced model development, system architecture improvements, faster processing times, and secure, reliable, efficient AI deployment. His work also includes leading data analysis, defining end-to-end ML pipelines, building ETL processes, and collaborating closely with business stakeholders to align technical solutions with strategic goals.
Yauheni holds an AWS Solutions Architect – Associate certification and a B.S. in applied mathematics and computer science. Committed to ongoing learning, he frequently explores new approaches in ML, NLP, and applied AI, and remains active in cross-team collaboration to support engineering excellence at Vention.
Tech expertise:
- Machine learning and NLP: PyTorch, Sklearn, CatBoost, LightGBM, XGBoost, YOLOv8, NER, classification, regression, forecasting, sentiment analysis
- Programming: Python, SQL, Bash
- Data engineering and analytics: Pandas, NumPy, Power BI, ETL pipelines, feature engineering
- Cloud and DevOps: AWS (EC2, S3, SQS), Azure Blob Storage, Docker, Kubernetes
- Databases: MySQL, PostgreSQL, SQLite, Oracle SQL, MongoDB
- Microservices and web: Flask, FastAPI, Django, BeautifulSoup
- System design and architecture: distributed processing, real-time analytics, API integration