Sajith Shaik

Built and modernized ML and data infrastructure using TensorFlow and PyTorch to improve model training efficiency and reduce production resource usage. Integrated data quality metrics into workflows to improve prediction reliability and system dependability. Designed data warehousing and Azure pipeline solutions using Azure Data Factory with Databricks and SQL, and implemented PySpark transformations with Delta Lake for curated datasets. Coordinated Azure Synapse and Power BI models with validation and access controls, and applied Git-based CI/CD practices to standardize releases.

Developed a feature engineering framework and improved data pipeline performance with better testing and validation, reducing processing time across distributed systems. Standardized SQL transformation documentation and validation checks to increase data processing speed and availability for analytics teams. Implemented ML workflows with scikit-learn to reduce inference and training time, and supported AI/ML use cases with faster deployment cycles. Built AWS Glue ETL jobs with S3 and Redshift, integrated Kafka-based streaming ingestion patterns, and automated Terraform plus CI/CD deployments for AWS data services.

Resolved operational issues by applying collaborative development practices and improving reliability and incident response for critical services. Optimized database performance by consolidating schemas and views, reducing query execution time. Built and maintained GCP analytics assets including BigQuery data marts and warehouse layers, and validated lineage, governance, and data quality rules to reduce reporting discrepancies. Orchestrated batch and real-time workflows using Airflow with Git and Agile delivery practices, improving scheduled workflow reliability and documentation quality.