Executive Director, Software Engineering AI Solutions

What matters most

• Turn AI opportunities into production products

  The posting makes this leader responsible for AI-powered products and platforms supporting Health100, including customer experiences, clinical workflows and operational efficiency. Prepare to explain how you would move a business opportunity from use-case selection through production delivery and measurable outcomes.

• Set engineering strategy and operating standards

  The role defines technical strategy, software standards, architecture governance and operational practices for secure, scalable, resilient applications. Prepare a concrete example of setting standards across multiple teams while retaining delivery accountability.

• Build reusable AI capabilities

  The posting calls for frameworks, services and accelerators that speed AI use cases across Health100, alongside modernization of existing applications. Prepare to distinguish what belongs in a shared platform from what should remain product-specific.

• Scale leaders and teams

  This executive director will recruit, mentor and develop engineering leaders and technical talent; the requirements cite leadership of 50+ engineers across multiple teams. Prepare examples of how you developed leaders and sustained quality and accountability as an organization grew.

• Align partners and communicate business value

  The role partners with Product Management, design, Security, Engineering and Infrastructure, and communicates roadmaps, risks and outcomes to executives. Prepare to show how you resolve competing priorities and connect technology investments to measurable business results.

Likely challenges to think through

• Balance AI innovation with production safeguards

  A likely challenge is moving quickly with Generative AI and agentic technologies while meeting the posting’s security, resilience and quality expectations. Prepare to discuss decision criteria and release safeguards; Responsible AI, model governance and AI security are preferred areas, not described as established employer processes.

• Reconcile reuse with varied use cases

  Shared AI services could accelerate delivery, but customer-facing products, clinical workflows and operational applications may need different designs. Prepare an example of choosing platform boundaries without forcing every use case into the same implementation.

• Modernize without disrupting delivery

  Adding AI-assisted capabilities to existing services while delivering new products may create competing demands on teams and architecture. Prepare to explain how you would sequence modernization, manage dependencies and make roadmap trade-offs visible to executives.

Topics to prepare

• Enterprise AI product strategy

  The role translates business opportunities into production-grade AI products supporting Health100. Revise by comparing two proposed AI use cases for customer experience or clinical workflows: explain their expected value, delivery risks and criteria for production readiness.

• Cloud-native architecture at scale

  Cloud-native platforms, distributed systems, APIs, microservices and software architecture are required expertise. Revise by sketching how a reusable AI service would integrate with existing applications, then explain scalability, resilience and maintainability trade-offs.

• Engineering governance and operations

  The role establishes software standards, architecture governance and operational practices for secure, resilient applications. Revise by outlining the decisions an architecture review should cover and how DevSecOps and ongoing support would be governed across teams.

• Scaling an engineering organization

  The posting requires experience leading organizations of 50+ engineers across multiple teams and calls for recruiting and mentoring leaders. Revise by mapping responsibilities, decision rights and coaching practices for several teams delivering a shared platform.

• AI governance and agentic systems

  LLMs, agentic architectures, Responsible AI, model governance and AI security are preferred, not required, qualifications. As preparation, compare a conversational or agentic workflow with a simpler AI-assisted workflow, including where human oversight and security review would matter.

Skills to revise

• Present an executive roadmap

  The role communicates strategy, roadmaps, risks and outcomes to executives. Practice a brief presentation that connects an AI product opportunity to delivery phases, major risks and measurable business outcomes; this is a preparation exercise, not a stated interview format.

• Run an architecture trade-off review

  The posting calls for architecture governance and delivery of secure, scalable AI applications. Practice reviewing a proposed API and service design: identify failure points, security concerns, operational ownership and the trade-offs behind a recommendation.

• Prioritize a reusable AI platform backlog

  The role establishes reusable frameworks, services and accelerators while partnering with Product Management. Practice ranking several shared-service requests against product needs, explaining what to build once, what to defer and how to measure value.

• Work through a cross-functional delivery decision

  The role collaborates with Product, design, Security, Engineering and Infrastructure. Practice facilitating a scenario in which a clinical-workflow feature has a delivery deadline and unresolved security or reliability concerns; state the decision, owners and escalation path.

• Map the stated qualifications

  Prepare a one-page evidence sheet for the posted 15+ years in technology leadership, 10+ years leading large-scale engineering organizations, 5+ years delivering enterprise AI or ML, leadership of 50+ engineers, and bachelor’s degree. Mark any experience that is relevant but not a direct match.

• Build a production AI project walkthrough

  Select a real AI or ML product you helped deliver and document the business problem, your decisions, path to production, operational support, and measured outcomes. If relevant, distinguish your work with LLMs, agents, or conversational AI; these are preferred, not required.

• Explain an enterprise architecture decision

  Prepare a diagram from a real cloud-native or distributed-system project showing APIs, services, scaling, resilience, DevSecOps, and architecture trade-offs. Identify what you personally decided and how the system was supported after launch.

• Practice a Health100-aligned design exercise

  Sketch a hypothetical AI-enabled improvement to a customer experience, clinical workflow, or operation—areas named in the posting. Show how reusable services, security, maintainability, and measurable value would shape the design; label the proposal as illustrative, not a CVS Health project.

• Document how you scaled engineering leadership

  Prepare a concrete account of organization structure, recruiting and mentoring leaders, engineering standards, architecture governance, and improvements you measured across teams. Separate decisions you owned from work delegated to others.

• Rehearse an executive roadmap discussion

  Draft a brief presentation that connects an AI opportunity to priorities, delivery milestones, risks, and outcome measures. Practice explaining how you would work with Product, design, Security, Engineering, and Infrastructure; the posting names these partners but does not prescribe a process.

• Prepare questions that test the role’s scope

  Ask how Health100 priorities are translated into AI product investments, what reusable platform capabilities already exist, how AI security and model governance are handled, and which outcomes the VP of Engineering expects this leader to influence.