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Lead MLOps and data engineering initiatives to operationalize AI/ML solutions using Snowflake, AWS, and Airflow.
Designed and deployed end-to-end ML pipelines leveraging AWS SageMaker, Docker, and Airflow.
Developed model monitoring dashboards using Streamlit and integrated metrics collection workflows.
Automated AWS infrastructure with Terraform, enabling scalable and reproducible deployments.
Built and containerized inference workflows, managing deployments via ECR, ECS, and Lambda.
Collaborated with data scientists to productionize ML models and deploy scalable, maintainable systems.
Designed data pipelines integrating Snowflake and orchestrated with Apache Airflow.
Developed Spark pipelines to support large-scale data processing.

Conducted exploratory data analysis in Jupyter to derive actionable insights.
Built interactive dashboards with Plotly, Streamlit, Power BI, and R Shiny.
Optimized SQL queries across SQL Server, Postgres, and Hive databases.
Developed Python-based ETL and reporting solutions.
Ensured data integrity through integration of disparate data sources.
Worked cross-functionally to translate business needs into data-driven outcomes.