This is the core difficulty of the role and we will probe it directlyDemonstrated ownership of CI/CD, release practice, and change management across more than one team - including the judgment calls about what ships and whenProduction experience architecting on a major cloud platform- Azure preferred, AWS or GCP acceptable - including cost, identity, networking, and the tradeoffs between managed services and self-hostedCurrent hands-on experience with modern application and API frameworks, sufficient to review a design and write code that other engineers take seriouslyWorking knowledge of data platform architecture: warehouse or lakehouse design, pipeline patterns, and the difference between a semantic layer that holds up and one that does notPractical experience applying AI to production problems - model selection and evaluation against a task, retrieval and classification patterns, and a clear view of where these approaches failExperience running change review or a comparable forum where sequencing and risk get arbitrated between teams with competing deadlinesHands-on software development experience current enough to be credible with practicing engineers - able to read and write production code, not only review architectureExperience making build-versus-buy decisions and living with the consequences of bothClear technical writing, plus the ability to explain a technical tradeoff to a non-technical executive audience without flattening itExperience in an environment where the architecture is partly inherited and cannot be replaced wholesale. Cloud and infrastructure- Microsoft Azure, with virtualization on Proxmox and VMware on premisesIdentity- Entra IDProductivity- Microsoft 365Business applications- Dynamics 365 Finance and Commerce, being replaced in stages; Dayforce for workforceData- Microsoft Fabric, lakehouse architecture, Power BI, and a semantic layer under active rebuildInternal build - web applications and services built in house, including integration and API layers between commercial platformsAI - large language models and classification models applied to operational problems, including model selection and evaluation against the task rather than by defaultEngineering practice- Git-based workflow, CI/CD, and infrastructure-as-code in varying stages of maturity.