AI Product Manager

2 days ago

Chennai, Tamil Nadu, India Valuebound Full time ₹ 10,000 - ₹ 50,000 per year

Description :

We are seeking a highly detail-oriented AI Product Manager Ops to own and drive operational excellence in data testing, validation, and monitoring of AI models in production environments.

This role sits at the intersection of product, data science, engineering, and operations.

Responsible for ensuring that AI-driven features perform reliably, safely, and responsibly at scale, while continuously improving product quality and customer experience.

Responsibilities :

- Reduction in production incidents caused by data/model issues.

- Establishment of reliable and scalable monitoring systems for AI products.

- Faster resolution time for data and model testing failures.

- Data Quality & Validation : Design and manage frameworks for data testing in production, ensuring high-quality, unbiased, and consistent datasets feed into AI models.

- Production Monitoring : Establish and monitor key health metrics (e.g., drift detection, anomaly detection, precision/recall, latency, and fairness) for AI systems in live environments.

- Testing Strategy : Develop and operationalize A/B testing and canary release strategies for model updates to mitigate risk.

- Incident Management : Lead root cause analysis and resolution of data-related production issues in collaboration with engineering, ML, and data science teams.

- Tooling & Automation : Drive requirements for automated testing pipelines, monitoring dashboards, and alerting systems.

- Cross-functional Collaboration : Work closely with product managers, ML engineers, data scientists, SREs, and compliance teams to ensure responsible AI deployment.

- Governance & Compliance : Ensure adherence to AI risk management, regulatory guidelines, and internal governance frameworks.

- Feedback Loop : Build mechanisms to capture customer and system feedback for continuous model improvement.

Skillset :

- Strong understanding of machine learning lifecycle, including data collection, training, validation, deployment, and monitoring.

- Proficiency in data analytics and SQL for diagnosing production issues and evaluating model performance.

- Knowledge of AI risk management, model governance, and responsible AI frameworks.

- Strong project management and communication skills - able to align cross-functional stakeholders and drive execution.

- Experience with A/B testing frameworks and canary deployments for model rollouts

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