Senior Director, AI Engineering and Delivery

Abbott•Chicago, IL

9d•$190,000 - $380,000

About The Position

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.

Requirements

15+ years of experience in software engineering and large-scale platform development.
Demonstrated success building and scaling enterprise platforms in financial services, fintech, or global technology firms.
Strong expertise in: Distributed systems and modern software architecture, Cloud platforms (AWS, Azure, GCP) in regulated environments, API, microservices, and event-driven architectures, Platform reliability, observability, and cost management
Proven track record delivering production AI and ML systems in real-world, regulated contexts.
Hands-on experience with: Machine learning lifecycle management (MLOps); Model monitoring, retraining, and performance management; Generative AI and foundation models (LLMs); RAG, prompt orchestration, evaluation, and guardrails; Experience operationalizing AI with risk controls, explainability, and governance.
Experience leading large, globally distributed engineering teams.
Strong stakeholder management skills across Technology, Risk, Compliance, and Business leadership.
Demonstrated ability to shift organizations toward platform-led, reuse-driven delivery models.
Track record of aligning AI platform investments to revenue growth, cost efficiency, risk reduction, or customer outcomes.
Bachelor’s degree in computer science, engineering, or a related technical discipline required.