• Architect, build, and maintain production-grade software (services, APIs, and web applications) with strong engineering discipline: code quality, testing, observability, and operational readiness. • Lead design reviews, establish engineering standards, and mentor engineers across multiple projects—especially software-heavy initiatives. • Build integration layers and service patterns that connect applications to data platforms, identity systems, and enterprise tooling. • Drive modern SDLC practices: trunk-based development or equivalent, strong code review culture, automated testing, and predictable release workflows. • Design and operate Kubernetes-based application platforms, including deployment standards, networking/ingress, upgrades, reliability, and day-2 operations. • Implement and mature CI/CD pipelines and release automation for both applications and infrastructure (including GitOps patterns where appropriate). • Build and maintain infrastructure-as-code (e.g., Terraform) and automated environment provisioning in Azure or AWS. • Lead engineers in deploying and maintaining enterprise platform tools such as Starburst, Immuta, Collibra, Databricks, Synapse, and related services. • Develop plans for cloud migrations and deployments and execute modernization strategies for application and data workloads. • Build and maintain data pipelines and supporting services that enable analytics, ML, and AI-enabled applications. • Partner with stakeholders to shape practical approaches to AI agents, AI infrastructure, and AI application enablement (e.g., orchestration patterns, retrieval/knowledge integration, evaluation and monitoring) in a secure environment.