Sr. Machine Learning Engineer
CurrentCollaborating with data architecture, data governance, and security teams to ensure solutions and meet required standards in the Solutions Architect Design document (SAD) for deploying Healthcare Recommender Systems in production, covering: • Content recommender • Collaborative filtering recommender • FAQs • Classes and Programs recommenderWorking with data scientists to design effective workflows and architectures for activating Recommender System ML models, optimizing impact in real-time streaming and offline batch scenarios.Implementing data processing workflows, incorporating necessary data cleansing and imputation logic.Addressing technical debt in ML infrastructure, focusing on enhancements to data pipelines, ML compute, data storage, and the Recommender system API, utilizing technologies like Azure Data Factory, Azure Machine Learning, Databricks, AKS, Cosmos DB, FASTAPI, and APIgee.Collaborating with the data engineering team to ensure timely delivery of data in the required format and cadence for ML model training.Experimenting with Large Language Models (LLM) models to effectively summarize and recommend healthcare articles.Member of the MLops council to set the MLops strategy and best practices for Kaiser Permanente.