Senior Director, AI Engineering and Delivery

The organization is making a strategic investment in AI and Generative AI and is creating a senior leadership role to architect, scale, and operationalize AI as a core platform capability.

This is a rare opportunity for a deeply technical, platform-oriented AI leader to shape how AI is engineered, governed, and consumed across a complex, regulated, multi business environment—moving the organization from pockets of innovation to enterprise-wide AI at scale.

The Head of AI Engineering and Delivery will lead the design, build, and evolution of enterprise AI and Generative AI teams and platforms for a global organization operating in life science, medical technology-driven markets. This leader will bring deep technical credibility across software engineering, data engineering, AI / machine learning, and cloud-native architecture, combined with a proven ability to build and lead technical teams operating within a highly regulated environment.

The role is responsible for creating reusable, secure, and scalable AI capabilities that empower product teams, business units, and operations to rapidly develop and deploy AI-driven solutions. The role will serve as a senior engineering and architecture authority for AI platforms, ensuring consistency, governance, and speed while enabling innovation across the enterprise.

• Build and lead a new AI Engineering & Delivery organization operating across three layers:
  Platform, Delivery, and Enablement
• Establish AI and GenAI as core enterprise platforms, not bespoke solutions.
• Enable self-service AI capabilities for product, engineering, and analytics teams.
• Balance innovation velocity with regulatory compliance and operational resilience.
• Drive measurable business outcomes across customer experience, risk, operations, and productivity.
• Build and lead delivery teams to execute on the strategic mandate, developing a future focused delivery operating model.

• Set and drive a unified, cross-business-unit AI platform strategy, ensuring seamless integration across products, services, and geographies
• Establish AI and GenAI as core enterprise platforms — not one-off solutions
• Champion API-first, platform-based architectures that accelerate time-to-market while reducing total cost of ownership
• Drive alignment across architecture proposals to maximize reuse, standardization, and leverage of shared AI and software services
• Plan and implement overall AI strategy; develop enterprise priorities and facilitate business and IT governance related to information design and business insight delivery

• Build and lead the AI Engineering & Delivery organization spanning Platform, Delivery, and Enablement
• Establish best-in-class delivery practices for AI, Software, and Data Engineering — including discovery, build, test, automation, validation, observability, and reliability
• Own the end-to-end AI and data engineering ecosystem: cloud-native platforms, AI/ML systems, connectivity, and secure data pipelines
• Drive end-to-end observability across data pipelines, model inference, tool execution, and agent outcomes — with clear SLIs/SLOs for quality, latency, reliability, and cost
• Standardize ML and agent development workflows to reduce time-to-production and eliminate bespoke infrastructure across teams

• Partner with business unit leaders to incubate, industrialize, and scale AI and Generative AI capabilities, including:

• Machine learning and advanced analytics
• GenAI copilots, autonomous agents, and intelligent assistants
• Agent lifecycle management: CI/CD, model registries, lineage, and access control
• RAG, prompt orchestration, evaluation, and guardrails
• Process optimization and reengineering
• Modern data science platforms and development frameworks

• Make agent evaluation and experimentation default platform capabilities — offline evaluation, pre-deployment quality gates and continuous post-deployment monitoring
• Translate innovation into production-grade, governed AI systems that deliver measurable business value

• Embed Responsible AI principles into…