• Own engineering delivery, maintenance, and production operations responsibilities for assigned areas across the HHAeXchange SaaS platforms. • Deliver new features and optimize existing components with focus on code quality, scalability, performance, security, stability, and maintainability. • Ensure effective incident response, troubleshooting, and operational execution for production systems. • Establish clear accountability and execution standards across engineering teams. • Provide leadership across multiple engineering teams, including engineering managers, tech leads, and individual contributors. • Drive predictable delivery through strong planning, prioritization, and execution discipline. • Set expectations for performance and accountability across teams. • Coach leaders and engineers through mentorship, feedback, and professional development. • Drive modernization and platform evolution, including cloud and architectural improvements. • Advance cloud-native principles and best practices in new development and enhancements. • Partner with architecture leadership to ensure system designs meet enterprise standards. • Promote continuous improvement through refactoring, technical debt reduction, and platform upgrades. • Lead engineering governance and operational readiness, ensuring consistency and quality across releases. • Participate in or support architecture review and design review processes. • Ensure proper release planning, change management, and production readiness practices. • Establish effective standards for deployment quality, rollback readiness, and operational support coverage. • Drive development and adoption of AI-enabled capabilities across the HHAeXchange SaaS platforms. • Partner with Product, Architecture, and Data/AI stakeholders to identify and deliver high-impact AI use cases that improve operational efficiency, compliance, and customer outcomes. • Ensure AI features are built with strong standards for quality, performance, security, privacy, and reliability. • Lead adoption of AI-assisted SDLC tools and practices to improve engineering velocity and effectiveness. • Establish best practices, guardrails, and repeatable workflows for AI-supported development, testing, code review, and documentation. • Measure and improve productivity and quality outcomes through responsible use of AI tooling while maintaining engineering standards and maintainability. • Work closely with VP, Software Engineering and CTO to formulate strategies and execute the vision. • Collaborate with Product Managers on future product strategy. • Collaborate and work with the team with a positive and open-minded attitude. • Share domain and technical expertise, providing technical mentorship and cross-training to other peers and team members. • Enable automation of configuration management of development, quality assurance, and production environments as well as automation of continuous integration and continuous delivery. • Supervise training and development of software engineering staff. • Support recruiting engineering personnel, technical staffing, and project teams’ formation.