Director, AI Engineering & Delivery

Vizient•Edina, MN

20h

About The Position

In this role, you will lead the execution, operationalization, scaling, and continuous improvement of enterprise AI engineering and delivery initiatives across Vizient. You will implement scalable AI engineering practices, AI delivery operating models, AIOps and LLMOps capabilities, and cross-functional engineering standards that enable the organization to rapidly and responsibly deliver AI-powered business outcomes, operational efficiencies, and scalable enterprise value. You will partner closely with business, technology, governance, automation, architecture, and quality engineering teams to build production-grade AI applications, agentic workflows, reusable platform capabilities, and operational processes that support Vizient’s enterprise AI transformation strategy.

Requirements

7 or more years of experience in software engineering, AI application engineering, engineering delivery, platform engineering, or enterprise technology functions required
3 or more years of experience leading engineering teams, delivery organizations, or large-scale technology initiatives required
Experience leading distributed teams, contractor/vendor coordination, and large-scale engineering delivery initiatives within complex and evolving operational environments required
Hands-on experience designing, delivering, and operationalizing production AI solutions leveraging large language models (LLMs), APIs, agentic workflows, orchestration frameworks, and modern AI engineering patterns required
Experience implementing and scaling engineering operating models, AI delivery frameworks, agile delivery ecosystems, or enterprise engineering practices required
Strong analytical, problem-solving, communication, presentation, stakeholder management, and cross-functional collaboration skills required
Ability to manage multiple priorities in fast-paced, evolving, and deadline-driven environments required
You must be authorized to work in the United States without sponsorship.

Nice To Haves

Relevant degree preferred
Experience with cloud platforms, APIs, data integration, DevOps practices, automation frameworks, and modern software engineering tools preferred
Experience operating in healthcare or other regulated environments preferred
Strong understanding of responsible AI concepts, including governance, human oversight, model monitoring, operational risk management, AI security considerations, and secure operationalization of AI solutions within enterprise environments preferred
Healthcare industry experience, including familiarity with HIPAA, SOC 2, or other regulated data environments preferred
Experience with AI-enabled automation, intelligent orchestration, evaluation pipelines, operational AI delivery practices, and agentic frameworks preferred
Demonstrated ability to balance execution excellence, operational scalability, governance, and engineering delivery effectiveness preferred

Responsibilities

Lead the execution and delivery of enterprise AI engineering initiatives, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and platform integrations
Drive day-to-day engineering delivery activities across AI teams, including sprint execution, backlog management, delivery tracking, issue resolution, dependency management, and operational execution
Implement and operationalize enterprise AI engineering practices, including AI software development lifecycle (SDLC) processes, deployment standards, runtime observability, release management, and engineering quality practices
Provide technical oversight across solution design, development, validation, deployment, monitoring, optimization, and production support activities
Support AIOps and LLMOps operational practices, including runtime monitoring, drift detection, observability, incident management, prompt lifecycle management, evaluation execution, operational telemetry, and production reliability
Develop reusable AI engineering patterns, implementation playbooks, shared services, templates, internal libraries, and engineering accelerators to improve delivery consistency, scalability, and operational efficiency
Drive adoption of enterprise engineering standards, scalable delivery practices, and shared implementation patterns across AI delivery teams
Partner with AI Governance, Quality Engineering, Automation, Architecture, and AI Delivery Lifecycle teams to operationalize governance requirements, validation processes, responsible AI controls, runtime safeguards, and secure delivery practices
Coordinate AI delivery activities across teams, including operational planning, resource management, contractor and vendor alignment, knowledge transfer, and delivery continuity
Partner with cross-functional stakeholders to support technical feasibility assessments, delivery readiness activities, implementation planning, and engineering sustainability efforts
Support vendor evaluations, platform implementation initiatives, build-versus-buy assessments, and engineering modernization efforts
Lead, mentor, and develop engineering managers, architects, engineers, and contractor teams while fostering a high-performing, collaborative, and continuously learning culture
Communicate delivery progress, operational risks, technical updates, engineering tradeoffs, and implementation recommendations to technical and business leaders
Research and evaluate emerging AI engineering, automation, observability, orchestration, and platform technologies to support innovation and continuous improvement