Director, I&T Plasma Engineering

The Director, I&T Plasma Engineering leads engineering and owns architecture governance across all CSL Plasma digital product domains. This role is internally focused on how the organization builds. Defining and owning the engineering standards, practices, and technology platform strategy that all development teams, managed service providers, and external vendors are held to. This role is also responsible for governing Artificial Intelligence (AI) and Generative AI in production across CSL Plasma. This spans AI features inside donor-facing and operational products, AI-assisted software development inside the engineering team, and the operational discipline required to run AI reliably at scale. The Director, I&T Plasma Engineering is distinct from product delivery coordination (owned by the Delivery Manager) and product strategy (owned by Sr. Directors and Product Owners). This role owns the technical foundation: the quality bar, the architectural patterns, the cross-cutting engineering capabilities, and the security and compliance posture that make reliable, scalable digital product delivery possible. This is a technical leadership role. The Director stays close enough to the work to be credible with the engineers doing it, contributes personally where judgment is required, and delegates confidently everywhere else. This position reports to: Senior Director, Plasma I&T Applications & Operations Reporting to this position: Plasma Business Architect, AI/ML Product Engineer, Automation Engineer, Data Engineer, SRE/DevOps Engineer, UX Designers Principal Accountabilities 1. Engineering Standards & Practices ·Own and define the 'how we build' standards across all five Plasma digital product domains: coding standards, testing standards, security standards (in formal matrix partnership with the CSL Cyber organization), DevOps and CI/CD practices, and architectural patterns. ·Set the technical quality bar that all internal delivery teams, managed service providers, and to which external vendors are contractually and operationally held. ·Enforce standards primarily through the shared engineering platform, automated quality gates, and review of significant technical designs. Architecture review boards are reserved for genuinely novel decisions, not routine sign-off. ·Reviews and approves significant technical designs personally, particularly those setting patterns others will follow. 2. Architecture Governance ·Set and enforce integration standards, data architecture, and cross-product technical decisions across the full Plasma digital ecosystem. ·Serve as the formal interface with the CSL Enterprise Architecture organization. Establish and maintain the cadence with the CSL Enterprise Architecture team, represent Plasma architecture upward, and bring enterprise decisions back to the Plasma team. Approve significant architectural decisions personally; delegate routine architecture work to domain technical leads with clear guardrails. ·Manage and develop the Plasma Business Architect, who executes day-to-day architecture work, alignment reviews, and architecture documentation. ·Owns the target-state architecture for the Data Backbone as an early priority given its role as the foundation for the AI, analytics, and operational data roadmap. 3. Cross-Domain Team Leadership ·Provide direct people leadership for cross-domain enabling functions: Plasma Business Architect, QA Lead, QA Automation Engineers, AI/ML Product Engineer, Automation Engineer, Data Engineer, SRE/DevOps Engineer, and UX Designers. ·Own hiring standards, role design, career development frameworks, and succession planning for the cross-domain engineering team. ·Set and hold a culture of technical excellence, accountability, psychological safety, and continuous learning across the engineering organization. ·Given the transformation of Plasma IT, personally interviews technical leads across the broader net-new hires, not only direct reports, to hold the hiring bar across the Plasma technology team. 4. Technology Platform Strategy ·Own the engineering tooling, platform, and capability roadmap — including the Data Backbone architecture, Middleware integration standards, cloud infrastructure direction, and the DevOps toolchain — in conjunction with enterprise capability owners. ·Ensure the technology platform evolves in step with the Plasma product roadmap, anticipating capability needs 12–24 months ahead. ·Evaluate build vs. buy vs. partner decisions for platform capabilities; provide technology investment recommendations to senior leadership. ·Owns the shared engineering platform that every Plasma product team builds on. This covers service templates, CI/CD pipelines, deployment patterns, observability, and security guardrails, delivered as a coherent product with a roadmap, an internal customer base across the domains, and adoption as the measure of success. The platform team builds it; the Director sets direction, approves the platform architecture, and reviews the highest-risk design decisions personally. 5. Technical Quality Oversight ·Review and approve technical approaches for significant architectural decisions across all Plasma digital products and vendor solutions. ·Serve as the escalation authority for technical quality issues that cannot be resolved within individual product delivery teams. ·Establish metrics for technical health: code quality, test coverage, lead time for changes, deployment frequency, change failure rate, and mean time to recovery (MTTR). Complement these delivery metrics with developer experience measures such as onboarding time, environment setup time, and developer satisfaction. ·Update engineering quality gates as AI coding assistants generate an increasing share of code, so that review depth, test coverage, and traceability scale with the change in how code is produced, 6. Artificial Intelligence Strategy & Governance ·Establish and execute an AI/Generative AI strategy for the engineering organization, identifying opportunities to apply AI technologies to accelerate delivery, improve quality, and enable new business capabilities. ·Drive the adoption of AI-assisted software development practices, including code generation, testing automation, documentation generation, and developer productivity tooling. ·Partner with business stakeholders, data teams, enterprise architecture, and security teams to identify, prioritize, and deliver AI-enabled solutions. ·Ensure AI solutions are developed and deployed in accordance with enterprise governance, regulatory, privacy, data protection, and responsible AI standards. ·Own the operational discipline for AI in production across CSL Plasma. This can includes model evaluation and evaluation harnesses, model observability and monitoring, prompt and version management, guardrail patterns and red-teaming, and cost management for inference and GPU spend. Established once as a shared capability and reused across deployed and planned Plasma AI products as well as subsequent AI use cases as the ecosystem matures. 7. Security & Compliance Standards ·Establish and own the formal matrix relationship with the CSL Cyber team for security and compliance standards across Plasma digital products. ·Ensure healthcare data protection, GDPR, and cloud security posture requirements are embedded in engineering standards — not treated as a separate audit activity. ·Oversee regulatory compliance engineering for applicable frameworks: 21 CFR Part 11, HIPAA, GxP, and EU Annex 11. ·Security controls are embedded in the shared engin