Director, AI Engineering & Delivery

Summary

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.

• 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

• Relevant degree preferred
• 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
• Ex…