Prodapt
Website:
prodapt.com
Job details:
Company Description Prodapt is a leading AI-first strategic technology partner focused on the Connectedness industry, delivering consulting, business reengineering, and managed services to major telecom and technology enterprises worldwide. The company helps build next-generation networks and digital experiences and has been recognized by Gartner as a large, telecom-native regional IT service provider. Prodapt connects over 1.1 billion people and 5.4 billion devices through a client portfolio that includes global leaders such as Google, Amazon, PayPal, Verizon, Vodafone, and Deutsche Telekom. With more than 6,000 technology and domain experts across the Americas, Europe, India, Africa, and Japan, Prodapt offers a diverse, global work environment. As part of the 130-year-old Jhaver Group and a Great Place To Work® Certified™ organization, Prodapt provides a stable, growth-oriented setting for professionals in AI and digital transformation.
Role Description:We are seeking a highly detail-oriented Al Product Manager - Ops to own and drive operational excellence in data testing, validation, and
monitoring of Al models in production environments.
This role sits at the intersection of product, data science, engineering, and operations.
Responsible for ensuring that Al-driven features perform reliably, safely, and responsibly at scale, while continuously improving product
quality and customer experience.
Key Responsibilities:
1. Data Quality & Validation: Design and manage frameworks for data testing in production, ensuring high-quality, unbiased, and
consistent datasets feed into Al models.
2. Production Monitoring: Establish and monitor key health metrics (e.g., drift detection, anomaly detection, precision/recall, latency,
and fairness) for Al systems in live environments.
3. Testing Strategy: Develop and operationalize A/B testing and canary release strategies for model updates to mitigate risk.
4. Incident Management: Lead root cause analysis and resolution of data-related production issues in collaboration with engineering,
ML, and data science teams.
5. Tooling & Automation: Drive requirements for automated testing pipelines, monitoring dashboards, and alerting systems.
6. Cross-functional Collaboration: Work closely with product managers, ML engineers, data scientists, SREs, and compliance teams to
ensure responsible Al deployment.
7. Governance & Compliance: Ensure adherence to Al risk management, regulatory guidelines, and internal governance frameworks.
- 8. Feedback Loop: Build mechanisms to capture customer and system feedback for continuous model improvement.
Click on Apply to know more.