Design and build end-to-end ML pipelines using GCP services (Vertex AI, BigQuery, Dataform)
Develop and productionize tabular ML models (e.g., XGBoost or similar)
Implement robust feature engineering pipelines with point-in-time correctness
Ensure reliable batch scoring workflows and production deployment
Partner with engineering and product stakeholders to translate business needs into ML solutions
Optimize data workflows for performance, scalability, and cost efficiency
Contribute to model evaluation, monitoring, and continuous improvement
Collaborate within a distributed team, ensuring clear communication and delivery alignment

Strong hands-on experience building and deploying ML solutions on GCP (Vertex AI ecosystem)
Proven track record delivering production ML models (not just experimentation)
Solid understanding of classification problems, especially imbalanced datasets
Metrics such as AUC-PR, ROC-AUC, precision@K, calibration
Experience designing feature pipelines with point-in-time accuracy
Advanced proficiency in BigQuery
Experience with data transformation tools (Dataform, dbt, or similar)
Strong Python skills for production systems pandas, scikit-learn, XGBoost, testing practices
Familiarity with ML lifecycle and MLOps practices

Senior Data Engineer – Machine Learning

Key skills