Kaan B

AI/ML Engineer & Data Scientist, FI-Espoo

As an AI & ML Engineer, I deliver end-to-end GenAI and ML solutions, bridging data engineering, advanced modeling, and user-facing applications. I have a proven record of building scalable, production-grade AI systems—from data pipelines and model training to real-time API and UI delivery. My expertise spans predictive modeling, data science, and large-scale data workflows, with a strong focus on automation and cost optimization.

✅ AI Solutions: Delivered end-to-end AI applications leveraging open-source frameworks and Azure AI Services. Developed AI solutions integrated into scalable systems and built front-end and back-end architectures (React & FastAPI), showcasing expertise in production-ready, user-centric AI applications.
✅ Computer Vision: Developed and fine-tuned YOLO models with PyTorch for efficient object detection, significantly boosting performance and resource utilization. (https://www.telia.fi/telia-yrityksena/medialle/epress?articleId=43c5024c-4a58-4218-97ae-e8fee02291e7)
✅ Energy-Saving ML Models: Spearheaded the creation of ML models for energy conservation, reducing carbon footprints, and optimizing energy use for repeatable routes. (https://www.rewake.fi/robi)
✅ ETL Process Automation: Automated ETL processes for billions of rows using Databricks, Snowflake, and Azure DE Services, streamlining data workflows.
✅ Stock Capacity and Sales Forecasting: Designed forecasting models processing billions of ERP data rows with Azure DS/ML Services, Databricks, Apache Spark, Snowflake, and MLflow.
✅ CI/CD Practices: Implemented CI/CD pipelines and robust quality assurance processes, ensuring data reliability and seamless integration across workflows.
✅ Predictive Modeling: Built predictive ML models in Python using AWS & Azure DS/ML Services, Databricks, MLflow, Airflow, Docker, and GitHub.
✅ Model Explainability for Financial Data: Developed a predictive model for the 'Model Explainability Toolbox for Financial Institutions' project. (https://decitech.com/model-explainability-toolbox-tips-techniques-interpret-black-box-models/)
✅ Cost Optimization: Reduced Databricks costs by 70% through optimized SQL, Python, PySpark code, clusters, and workflows.
✅ Education & Knowledge Sharing: Passionate about mentoring aspiring data professionals, and teaching DS, ML, and MLOps courses while fostering continuous learning.
✅ Above all, I am committed to innovation, continuous improvement, and professional growth.

Asking target price: 107 €/h.