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 platform design and engineering practices from the start
• Partner with Risk, Compliance, Legal, and Security to ensure model governance, lifecycle controls, and regulatory compliance across jurisdictions
• Ensure AI-enabled systems meet enterprise standards for security, performance, resilience, and regulatory compliance — including FDA, SOX, MoH, and regulations applicable to pharmaceutical, food, and medical device industries
• Implement and maintain compliance controls and policies applicable to pharmaceutical, food, and medical device industries
• Act as a senior voice in AI risk and governance forums across the enterprise

• Recruit, develop, and retain world-class technical talent; foster a culture of excellence, accountability, and continuous learning
• Provide clear leadership, mentoring, and guidance to senior leaders, principal engineers, and architects across the enterprise
• Act as a connective force across Technology, Product, Operations, Cybersecurity, Compliance, and Commercial teams
• Serve as a trusted advisor to executive leadership on technology strategy, investment decisions, and transformation roadmaps
• Work in partnership with business and IT to govern total cost of investment for existing reporting environments with a focus on standardization and consolidation

A unified AI and GenAI platform is live and broadly adopted across the enterprise. Product and business teams can rapidly build AI capabilities using standardized services. AI risk, governance, and compliance are embedded by design, not retrofitted. AI engineering is viewed as a strategic technology capability enabling speed, safety, and scale, delivering measurable outcomes.

• Bachelor’s degree required (Business, Computer Science, Engineering, Data/Analytics, or related)
• 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.
• Proven leader of large, global, multidisciplinary teams
• Platform mindset with a bias toward reuse, leverage, and scale
• Clear communicator who can translate complexity into executive-level decisions.
• Comfortable operating in highly regulated, high-stakes environments.

In specific locations, the pay range may vary from the range posted.