I am a Machine Learning Engineer focused on building scalable, reproducible, and production-ready ML & MLOps pipelines.
With over a decade of technical experience, I combine a systems-oriented mindset with business impact to deliver real-world AI solutions.

• Milk Price Prediction
  End-to-end MLOps pipeline with Prefect, MLflow, Evidently, and AWS Lambda. Includes drift monitoring, model registry, CI/CD, and batch + on-demand inference.

• Predictive Maintenance
  Applied feature engineering, ML pipelines, and explainability techniques (SHAP, PDPs) to anticipate failures in industrial machines.

• Brain Cancer Detection
  Brain Cancer Detection with CNNs.

• Ecobici Data Engineering
  Complete data pipeline with Terraform, Kestra, dbt, and GCP to process public bike-sharing data and publish dashboards in Looker Studio.

• ML & MLOps: TensorFlow, XGBoost, MLflow, Hyperopt, Evidently
• Orchestration & Data Engineering: Prefect, Kestra, dbt, Terraform
• Cloud: AWS (Lambda, S3, ECR, API Gateway), GCP (BigQuery, Cloud Storage, Looker Studio)
• Data & BI: Python, SQL, Power BI, Tableau

• AWS Machine Learning Engineer – Associate
• Google Cloud Digital Leader
• Microsoft Power BI Data Analyst Associate
• ITIL 4 Specialist – Plan, Implement and Control
• MLOps Zoomcamp (DataTalks Club)
• Machine Learning Zoomcamp (DataTalks Club)

Always open to collaboration:
Let’s connect if you want to tackle complex data challenges, build production ML pipelines, or contribute to open-source AI projects.