Deploy the ML models developed by data scientists in the production systems using GCP, BigQuery, and Airflow
Collaborate with data scientists to seamlessly integrate model code (Python/PySpark) into the MLOps workflow.
Resource and code Optimization of the ML model prediction scoring pipelines in GCP ecosystem so that the models run within the defined SLA
Debug the ML model scoring failures for code and resource issues
Orchestrate the model scoring pipelines using Cloud Composer
Deploy Auto-ML solutions in the production systems
Implement model performance monitoring (LIFT, ROC, Accuracy, and so), model stability monitoring, and feature/concept drift monitoring for all the new models
Implement modernization practices such as observability and explainability for enhanced model monitoring and interpretability.
Work closely with Responsible AI teams to ensure ethical AI principles and incorporate fairness, transparency, and accountability.
Utilize Jenkins and GitLab for effective code management and version control.
Leverage Vector DB(Milvus or any other), and LLM services in order to deploy LLM models