VP, Head of AI Center of Excellence (AI COE)

Role Summary

The VP, Head of AI CoE will focus on enabling consistent, high-quality AI capabilities, including large language model (LLM) access, that can be confidently adopted across all Pfizer divisions. The role is designed to bring greater clarity, prioritization, and operating rigor to the AI CoE, ensuring that talent, platforms, and investments are aligned to Pfizer’s most important business outcomes. This position requires a leader who can combine technical depth, strategic thinking, and enterprise influence to help the AI CoE mature from a growing capability into a core, mission-critical function for the company.

Responsibilities

• Build and operate secure, reliable, low-latency AI/LLM platforms with clear SLAs/SLOs, cost and performance guardrails, and strong observability.
• Standardize access to approved foundation models and internal models, enabling consistent enterprise use while meeting compliance and privacy requirements.
• Drive architectural simplification, platform reuse, and a catalog of reusable AI services that accelerate delivery across functions.
• Define a multi-year AI CoE strategy and roadmap aligned to enterprise priorities; make decisive trade-offs that focus talent and investment on the highest-value outcomes.
• Shift from project-by-project execution to platforms, products, and capabilities with measurable value realization and transparent portfolio governance.
• Establish outcome-based funding, clear ownership, and cadence for performance reviews to ensure delivery at scale.
• Partnering closely with Legal, Compliance, Privacy, and Security, embed Responsible AI by design: model risk management, validation, auditability, explainability, human-in-the-loop controls, and regulatory readiness.
• Ensure equitable access to AI platforms, tools, and enablement so teams across geographies and functions benefit from shared capabilities and standards.
• Serve as a trusted advisor to senior leaders across the enterprise to align initiatives, remove roadblocks, and accelerate adoption.
• Communicate a clear vision for how AI accelerates Pfizer’s scientific and operational breakthroughs; champion responsible speed in a regulated environment.
• Raise the engineering and leadership bar: recruit, develop, and retain top AI platform engineers, ML engineers, applied scientists, and AI product leaders.
• Set standards for software quality, MLOps/LLMOps, model lifecycle management, security, and SRE practices; promote craftsmanship and continuous learning.
• Foster a culture of pride, recognition, collaboration, and learning that enables teams to do their best work together.

Qualifications

• Required: BS/BA degree required, higher degree preferred or relevant experience, 15+ years in technology/AI/advanced analytics leadership, including building and running enterprise-scale AI/ML or data platforms with high reliability and performance.
• Required: Deep understanding of LLMs/foundation models, ML systems architecture, MLOps/LLMOps, security, and modern cloud platforms.
• Required: Proven ability to shape strategy, prioritize portfolios, and influence senior executives in complex, matrixed organizations.
• Required: Experience operating in highly regulated environments (e.g., life sciences, healthcare, financial services) with strong compliance and audit disciplines.
• Required: Demonstrated track record of building high-performing engineering organizations and cultivating inclusive, collaborative cultures.
• Required: Significant experience with MLOps frameworks (e.g., Kubeflow, SageMaker, MLflow) and a deep understanding of the inference stack (GPU orchestration, quantization techniques, and token-optimization).
• Required: Expert-level knowledge of data privacy, security, and ethics in a regulated environment (HIPAA, GDPR, GxP).
• Preferred: Prior leadership of an AI Center of Excellence or platform product organization supporting multiple business units.
• Preferred: Experience translating advanced science and technology into enterprise-ready platforms adopted at scale across R&D, Manufacturing, and Commercial.
• Preferred: Familiarity with Responsible AI frameworks, model risk governance, and validation approaches suitable for regulated use cases.

Additional Requirements

• Travel up to 20% may be required for business activities.