Ajay Kumar  ·  Senior Azure / AWS Data Engineer  ·  6+ yrs

Overview: An Azure-based data pipeline architected to handle millions of healthcare records in various formats, enabling real-time and batch processing for timely insights and actionable intelligence. Responsibilities: Developed Python scripts for data extraction from gz format files and loading into Azure Data Lake Gen 2. Utilized Azure Data Factory, Azure Databricks, and Delta Lake for regular ETL tasks and implemented a data cleaning framework.

Overview: Implemented an event-driven e-commerce data pipeline on AWS, leveraging EventBridge for triggering scheduled workflows and integrated various AWS services for efficient data processing. Responsibilities: Developed the data pipeline on AWS using EventBridge for workflow triggers and integrated S3 storage, Glue Crawler, and ETL jobs.

Overview: A real-time safety monitoring system implemented with machine learning to process live video feeds and detect workers without proper safety gear. Responsibilities: Led the development and optimization of machine learning models for image recognition, including object detection algorithms to identify safety gear violations.

• A restaurant wait list management app enabling customers to be added to a waitlist and receive free text messages when their tables are ready.

• Developed a Python script for extracting gz format files, converting them to CSV or JSON, and loading data into Redshift.
• Analyzed user specifications and requirements.
• Developed the code as per the requirements.
• Developed full back-end business logic and performed required database operations.

• A real-time safety monitoring system implemented with machine learning to process live video feeds and detect workers without proper safety gear.

• Led the development and optimization of machine learning models for image recognition, including object detection algorithms to identify safety gear violations.
• Applied computer vision techniques to analyze live camera feeds, crafting algorithms for identifying individuals not wearing safety gear.
• Established data pipelines for streaming and storing live camera feeds, upholding data quality and reliability standards.
• Led the design and development of the Django-based dashboard, integrating ML and computer vision results, and implementing user authentication.
• Oversee deployment and management of live camera feed infrastructure, establishing scalable and reliable cloud services.