Director, AI Execution Assurance & Operations

Vizient•Edina, MN

3d•$135,200 - $236,600

About The Position

In this role, you will provide enterprise-wide technical leadership to integrate, assess, and optimize AI capabilities across Vizient’s AI Optimization & Delivery organization. You will translate enterprise AI priorities into an integrated capability roadmap, technical operating model, and optimization recommendations that accelerate governed AI adoption, strengthen quality and reliability, increase reuse, and improve investment efficiency. You will collaborate with senior technical and functional leaders to identify systemic gaps, evaluate emerging technologies, and deliver actionable recommendations that advance scalable, secure, and measurable AI capabilities across the enterprise.

Requirements

Relevant degree preferred.
7 or more years of relevant experience required.
Extensive experience in software engineering, platform engineering, cloud architecture, AI/ML, or enterprise technology transformation, including senior technical leadership.
Demonstrated experience shaping enterprise technology strategies, capability roadmaps, technical operating models, and large-scale platform modernization initiatives.
Strong understanding of enterprise AI technologies, including large language models (LLMs), agentic systems, retrieval-augmented generation (RAG), model gateways, orchestration, evaluation frameworks, and AI governance.
Deep knowledge of cloud-native architectures, distributed systems, APIs, microservices, containers, infrastructure as code, CI/CD, DevSecOps, and production reliability engineering.
Experience integrating cross-functional technology roadmaps, assessing capability maturity, identifying systemic constraints, and developing enterprise optimization recommendations.
Strong understanding of AI quality, observability, security, responsible AI practices, operational risk management, and governance requirements in regulated environments.
Experience evaluating technology investments, AI unit economics, platform efficiency, and build, buy, partner, consolidate, or retire decisions.
Demonstrated ability to establish enterprise capability measurement frameworks, improve technology reuse, reduce duplication, and evaluate operational and business value.
Excellent executive communication, analytical problem-solving, and influencing skills, with the ability to align senior technical leaders and drive strategic recommendations without direct authority.

Nice To Haves

Experience with enterprise cloud and platform technologies such as Azure, AWS, Kubernetes, OpenShift, Terraform, and modern observability tools preferred.

Responsibilities

Develop and maintain an 18- to 24-month enterprise AI capability roadmap recommendation integrating functional priorities, technical dependencies, maturity, risks, and investment opportunities.
Evaluate AI capabilities across engineering delivery, AI Factory, application operations, quality and reliability, tooling, automation, clinical data, and governance to identify gaps, duplication, and optimization opportunities.
Establish cross-functional capability and ownership models that clarify decision rights, service interfaces, technical dependencies, contribution pathways, and operational handoffs.
Analyze emerging AI technologies, platform capabilities, and engineering practices to recommend opportunities for innovation, standardization, investment, consolidation, or retirement.
Develop organization-level maturity and performance measurement frameworks encompassing AI adoption, enterprise reuse, delivery efficiency, quality, reliability, governance, cost, and engineering experience.
Assess production performance and operational trends to distinguish systemic capability constraints from functional delivery issues and recommend improvements to accountable leaders.
Evaluate AI capability investments and unit economics to develop fact-based build, buy, partner, simplify, and optimization recommendations aligned with enterprise objectives.
Define and recommend technical operating mechanisms that strengthen cross-functional collaboration, exception management, capability reviews, and production feedback loops.
Facilitate alignment on reusable AI components, engineering patterns, evaluation frameworks, governance controls, and operational practices to reduce duplication and accelerate governed production adoption.
Identify opportunities to improve engineering experience, capability discoverability, adoption, and cross-functional workflows while reducing rework, operational friction, and technical complexity.
Partner with Solutions Architecture, functional AI leaders, and Technical Program Management to align capability recommendations with approved architecture, functional strategies, and execution ownership.
Advise executive and senior technical leadership on AI capability decisions, investment tradeoffs, organizational risks, and value realization through evidence-based analysis and actionable recommendations.