Faiz Ahamed Shaik

About Faiz Ahamed Shaik

Full‑stack / backend engineer building ApplyZap as a founder—shipping production features…

Experience

1. Ai Ml Engineer

   Kaiser Permanente

   Feb 2024 — Present · Oakland, CA, US

   Designed a patient readmission risk prediction model that improved risk stratification accuracy by 25% by implementing XGBoost and Random Forest ensembles on EHR data.• Automated feature engineering for multi-modal clinical data, cutting pipeline execution time by 30% and ensuring full HIPAA compliance across all data handling processes.• Fine-tuned BERT models using TensorFlow to extract medical entities from clinical notes, automating a manual process and reducing coding time by 15 hours per week.• Deployed containerized models on Azure Kubernetes Service (AKS), establishing a continuous retraining pipeline with Azure ML that reduced model update cycles from weeks to days.• Validated model performance with clinical teams, integrating predictive analytics directly into patient care workflows to support treatment decisions.• Implemented model monitoring with Azure Monitor and Evidently AI to track data drift and performance decay, ensuring over 99% inference reliability.• Optimized Spark jobs in Databricks to process terabytes of patient data, improving ETL throughput by 35% and enabling faster feature set generation.• Developed a feature store to standardize and serve pre-computed features across multiple ML projects, reducing redundant computation by 40%.• Created comprehensive model documentation and API specifications for integration teams, accelerating deployment timelines by two weeks.• Mentored two junior data scientists on software engineering best practices and MLOps principles, improving team productivity by 20%.

Education

• KL University

  Bachelor of Technology - BTech, Computer Science

• San José State University

  Master's degree, Computer Software Engineering