Senior Director, AI Engineering and Delivery

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.

     

JOB DESCRIPTION:

Executive Summary

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.

Strategic Mandate

• 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.

Key Responsibilities

Define & Execute AI Platform Strategy

• 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 & Scale AI Engineering 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

Enable GenAI & Emerging Technology at Scale

• 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