AI Data Operations Analyst

Key Skills: Azure, Machine Learning, Data Management, Python, Team Management.

Roles & Responsibilities:

• Develop and implement AI data operations strategy and quality standards.
• Design and implement comprehensive data quality monitoring systems.
• Establish data validation workflows and acceptance criteria.
• Create efficient processes for data acquisition, labeling, and validation.
• Guide development of data operation automation tools.
• Lead architectural decisions for scalable and efficient data infrastructure.
• Oversee data labeling operations and manage vendor relationships.
• Implement and maintain robust data quality assurance processes.
• Develop tools and scripts for data operations automation.
• Create monitoring dashboards and quality metrics systems.
• Manage dataset versions and release processes.
• Drive continuous improvement in data quality and operational efficiency.
• Implement systems for tracking data inventory and usage.
• Establish and monitor SLAs for data operations.
• Design and optimize data pipeline efficiency.
• Drive cost optimization for data operations.
• Create automated quality control processes.
• Establish metrics collection and reporting infrastructure.
• Collaborate closely with ML engineers, data scientists, and DevOps teams.
• Contribute to continuous improvement of data practices across the ML lifecycle.

Experience Required:

• 7-10 years of experience in managing AI/ML data pipelines in production environments.
• Proven expertise in handling large-scale data operations using Azure and Python.
• Hands-on experience with designing and implementing automated data validation and labeling workflows.
• Experience with monitoring data quality, dataset versioning, and SLA tracking.
• Demonstrated ability in leading cross-functional teams and managing vendor operations.

Education:  B.Tech M.Tech (Dual), MCA, B.Tech, M. Tech