AI Pipeline Lead
The AI Pipeline Lead is responsible for orchestrating core AI operations and governance processes across the enterprise, ensuring that all artificial intelligence initiatives are systematically assessed, prioritized, approved, deployed, and monitored in alignment with AI governance processes, business value, regulatory requirements, and ethical standards.
In support of the AI pipeline, this leader owns the AI intake and demand management process, operationalizes AI stage gates, and partners closely with business leaders, IT, compliance, privacy, cyber & information security, and execution teams to ensure AI solutions are safe, effective, and production ready. The role plays a pivotal function in enabling responsible AI innovation while protecting member, provider, customer and organizational trust in a highly regulated healthcare environment.
AI Stage Gates
- Manage and document stage-gate reviews for each AI use case across the full AI lifecycle from ideation and design through validation, deployment, and postproduction monitoring. gate reviews production monitoring.
- Ensure that each stage gate meets required criteria for advancement, including technical readiness, ethical risk assessment, privacy and security controls, regulatory considerations, value realization, and business adoption readiness.
- Support the maturation of the AI stage gate process in alignment with portfolio management and enterprise Program Management Office initiatives.
Deployment Readiness & Controlled Release
- Work closely with AI Foundry and platform teams to support controlled releases, pilot evaluations, and production rollouts.
- Verify that AI solutions meet predefined performance, bias, safety, explainability, value gates, and monitoring criteria prior to deployment.
- Ensure clear documentation, approvals, and operational handoffs are completed before transitioning AI solutions to business operations.
Post Deployment Monitoring & Adoption Deployment Monitoring & Adoption
- Partner with AI solution owners and platform teams to oversee post deployment monitoring for model performance, value realization, drift, bias, data integrity, and unintended outcomes. deployment monitoring for model performance
- Support adoption planning, including change management, user enablement, and ongoing governance checkpoints.
- Facilitate feedback loops to inform retraining, remediation, or retirement decisions as part of continuous AI lifecycle management.
Stakeholder Engagement & Communication
- Serve as a trusted liaison between business leaders, technical teams, and governance bodies.
- Communicate complex AI lifecycle, risk, and governance concepts in clear, actionable terms to non-technical stakeholders.
- Support executive and committee level reporting on AI demand, pipeline health, risk posture, and deployment status.
Regards,
Raj Dakshinapu | Recruiter, Dotcom Team LLC
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