AVP, AI Engineering & Delivery

Vizient•Edina, MN

About The Position

Lead enterprise AI engineering and delivery initiatives across Vizient, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and API/platform integrations. This role involves operationalization practices for enterprise AI solutions and establishing and maturing enterprise AI engineering and delivery capabilities. This includes AI-native development practices, AI SDLC frameworks, scalable operational models, observability, runtime reliability, deployment standards, and reusable engineering accelerators that enable the industrialization of AI solutions across the enterprise. The position will help evolve enterprise engineering and delivery practices to support AI enabled workflows, automation patterns, modern software delivery models, and continuously improving operational maturity. The role also entails leading technical reviews and providing oversight for enterprise AI initiatives across design, build, validation, deployment, monitoring, optimization, and production support activities.

Requirements

Lead enterprise AI engineering and delivery initiatives across Vizient, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and API/platform integrations.
Operationalization practices for enterprise AI solutions.
Establish and mature enterprise AI engineering and delivery capabilities, including AI-native development practices, AI SDLC frameworks, scalable operational models, observability, runtime reliability, deployment standards, and reusable engineering accelerators that enable the industrialization of AI solutions across the enterprise.
Help evolve enterprise engineering and delivery practices to support AI enabled workflows, automation patterns, modern software delivery models, and continuously improving operational maturity.
Lead technical reviews and provide oversight for enterprise AI initiatives across design, build, validation, deployment, monitoring, optimization, and production support activities.
Lead AIOps and LLMOps operational capabilities, including runtime observability, drift detection, monitoring, incident management, prompt lifecycle management, evaluation frameworks, operational telemetry, output reliability, and AI-specific operational risk management.
Drive foundational platform capabilities, including reusable AI engineering patterns, implementation playbooks, shared services, evaluation pipelines, templates, internal libraries, and engineering accelerators that improve delivery consistency, scalability, enterprise-wide AI delivery maturity, and long-term operational efficiency.
Promote reusable engineering standards, scalable delivery practices, and shared implementation patterns that reduce per-use-case engineering effort and improve operational efficiency across the enterprise.
Partner with AI Governance, Quality Engineering, Automation, and AI Delivery Lifecycle teams to support enterprise AI lifecycle management, validation frameworks, governance processes, responsible AI practices, human oversight controls, operational safeguards, and secure operationalization of AI solutions.
Lead AI delivery portfolio management activities, including squad capacity planning, vendor and contractor management, forecasting, operational optimization, runtime efficiency, scalable delivery execution, knowledge transfer, and sustainable engineering operations.
Partner with cross-functional teams to evaluate technical feasibility, scalability, operational readiness, engineering sustainability, and modernization opportunities for prioritized AI initiatives.
Support build-versus-buy evaluations, vendor assessments, platform selection activities, and enterprise engineering modernization initiatives.
Lead, mentor, and develop engineering leaders, architects, engineers, and contractor teams while fostering a high-performing, collaborative, continuously learning, and delivery-focused engineering culture.
Communicate technical concepts, delivery risks, operational updates, engineering tradeoffs, and strategic recommendations effectively to technical and executive stakeholders to support informed decision-making, enterprise alignment, and successful business adoption of AI capabilities.
Bring product judgment and strategic thinking to use-case intake — create and manage rigorous scoping and prioritization capability.
Research and evaluate emerging AI engineering, automation, observability, orchestration, and operational technologies to support innovation and continuous improvement initiatives.

Responsibilities

Lead enterprise AI engineering and delivery initiatives across Vizient, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and API/platform integrations.
Operationalization practices for enterprise AI solutions.
Establish and mature enterprise AI engineering and delivery capabilities, including AI-native development practices, AI SDLC frameworks, scalable operational models, observability, runtime reliability, deployment standards, and reusable engineering accelerators that enable the industrialization of AI solutions across the enterprise.
Help evolve enterprise engineering and delivery practices to support AI enabled workflows, automation patterns, modern software delivery models, and continuously improving operational maturity.
Lead technical reviews and provide oversight for enterprise AI initiatives across design, build, validation, deployment, monitoring, optimization, and production support activities.
Lead AIOps and LLMOps operational capabilities, including runtime observability, drift detection, monitoring, incident management, prompt lifecycle management, evaluation frameworks, operational telemetry, output reliability, and AI-specific operational risk management.
Drive foundational platform capabilities, including reusable AI engineering patterns, implementation playbooks, shared services, evaluation pipelines, templates, internal libraries, and engineering accelerators that improve delivery consistency, scalability, enterprise-wide AI delivery maturity, and long-term operational efficiency.
Promote reusable engineering standards, scalable delivery practices, and shared implementation patterns that reduce per-use-case engineering effort and improve operational efficiency across the enterprise.
Partner with AI Governance, Quality Engineering, Automation, and AI Delivery Lifecycle teams to support enterprise AI lifecycle management, validation frameworks, governance processes, responsible AI practices, human oversight controls, operational safeguards, and secure operationalization of AI solutions.
Lead AI delivery portfolio management activities, including squad capacity planning, vendor and contractor management, forecasting, operational optimization, runtime efficiency, scalable delivery execution, knowledge transfer, and sustainable engineering operations.
Partner with cross-functional teams to evaluate technical feasibility, scalability, operational readiness, engineering sustainability, and modernization opportunities for prioritized AI initiatives.
Support build-versus-buy evaluations, vendor assessments, platform selection activities, and enterprise engineering modernization initiatives.
Lead, mentor, and develop engineering leaders, architects, engineers, and contractor teams while fostering a high-performing, collaborative, continuously learning, and delivery-focused engineering culture.
Communicate technical concepts, delivery risks, operational updates, engineering tradeoffs, and strategic recommendations effectively to technical and executive stakeholders to support informed decision-making, enterprise alignment, and successful business adoption of AI capabilities.
Bring product judgment and strategic thinking to use-case intake — create and manage rigorous scoping and prioritization capability.
Research and evaluate emerging AI engineering, automation, observability, orchestration, and operational technologies to support innovation and continuous improvement initiatives.