AVP, AI Engineering & Delivery

Vizient•Edina, MN

2d•$156,500 - $290,100•Onsite

About The Position

This leadership role focuses on driving enterprise AI engineering and delivery initiatives. The position involves leading AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and API/platform integrations. A key aspect is establishing and maturing 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. The role also involves evolving engineering and delivery practices to support AI-enabled workflows, automation patterns, modern software delivery models, and improving operational maturity. Additionally, the position requires leading technical reviews and providing oversight for enterprise AI initiatives throughout their lifecycle, from design to production support.

Requirements

Lead enterprise AI engineering and delivery initiatives
AI-powered applications
LLM-enabled workflows
Agentic orchestration solutions
AI-enabled automation capabilities
API/platform integrations
Operationalization practices for enterprise AI solutions
Establish and mature enterprise AI engineering and delivery capabilities
AI-native development practices
AI SDLC frameworks
Scalable operational models
Observability
Runtime reliability
Deployment standards
Reusable engineering accelerators
Industrialization of AI solutions
Evolve enterprise engineering and delivery practices
AI enabled workflows
Automation patterns
Modern software delivery models
Continuously improving operational maturity
Lead technical reviews
Oversight for enterprise AI initiatives
Design, build, validation, deployment, monitoring, optimization, and production support activities
Lead AIOps and LLMOps operational capabilities
Runtime observability
Drift detection
Monitoring
Incident management
Prompt lifecycle management
Evaluation frameworks
Operational telemetry
Output reliability
AI-specific operational risk management
Drive foundational platform capabilities
Reusable AI engineering patterns
Implementation playbooks
Shared services
Evaluation pipelines
Templates
Internal libraries
Engineering accelerators
Delivery consistency
Scalability
Enterprise-wide AI delivery maturity
Long-term operational efficiency
Promote reusable engineering standards
Scalable delivery practices
Shared implementation patterns
Reduce per-use-case engineering effort
Improve operational efficiency
Partner with AI Governance, Quality Engineering, Automation, and AI Delivery Lifecycle teams
Support enterprise AI lifecycle management
Validation frameworks
Governance processes
Responsible AI practices
Human oversight controls
Operational safeguards
Secure operationalization of AI solutions
Lead AI delivery portfolio management activities
Squad capacity planning
Vendor and contractor management
Forecasting
Operational optimization
Runtime efficiency
Scalable delivery execution
Knowledge transfer
Sustainable engineering operations
Partner with cross-functional teams
Evaluate technical feasibility
Scalability
Operational readiness
Engineering sustainability
Modernization opportunities
Prioritized AI initiatives
Support build-versus-buy evaluations
Vendor assessments
Platform selection activities
Enterprise engineering modernization initiatives
Lead, mentor, and develop engineering leaders, architects, engineers, and contractor teams
Foster 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
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
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.