Scaled AI & Solution Architect

The Scaled AI & Solution Architect is the senior technical design authority for the Digital Quality (DQ) organisation, which delivers the solutions that transform Quality, Safety and Environmental Operations (QSEO) across Pfizer. The role is a global individual contributor position accountable for setting and driving the AI and solution architecture direction across the entire DQ product portfolio, rather than for the delivery of any single product.

The incumbent defines the target architecture, reference patterns, reusable components and engineering standards that every DQ solution is built against, so that capability is built once and reused many times rather than rebuilt product by product. The role leads the scaled adoption of artificial intelligence (AI) and agentic AI across the portfolio, translating enterprise AI platforms and emerging technology into GxP-compliant, validated, production-grade capability that measurably improves quality, safety and environmental outcomes at site level.

Recognised as an expert within and beyond Digital Quality, the incumbent influences the strategic technology direction of the QSEO business, and partners with Solution Leads, the Platform and Solution Engineering team, Enterprise Architecture, Cyber Security to ensure architectural coherence, compliance and pace across a complex global portfolio.

Role Responsibilities:

• Own the target-state solution architecture for the Digital Quality portfolio, spanning quality management, laboratory, manufacturing quality, and safety, environment and occupational health solutions.
• Define how products, platforms, data and integrations fit together, and drive convergence of point solutions onto common architecture, avoiding duplicated capability and unmanaged technical debt across the portfolio.
• Establish, publish and maintain the architecture standards, reference patterns, reusable components and engineering guardrails that DQ solutions are designed and built against, covering integration, data, identity, AI, validation and cyber security.
• Make reuse the path of least resistance for delivery teams, and measure adoption, reuse and the resulting delivery efficiency.
• Define and drive the AI and agentic AI architecture for the DQ portfolio, including use case patterns such as deviation and incident triage, leading-indicator prediction, automated document and content generation, and conversational access to standards and procedures.
• Determine where AI capability should be built once as a shared service versus configured per product, and ensure AI is delivered on enterprise platforms with appropriate human oversight, evaluation, model monitoring and lifecycle management.
• Act as design authority for significant DQ investments, reviewing and approving solution designs at key lifecycle gates and providing architectural assurance on build-versus-buy, platform selection and vendor technical fit.
• Identify multiple viable options for highly complex problems, set out the trade-offs in cost, risk, compliance and time to value, and make clear recommendations to Digital and business leadership and relevant governance forums.
• Provide hands-on architectural leadership to Solution Leads, Product Managers, engineers and vendor teams across the portfolio without direct reporting authority, influencing through expertise, credibility and evidence.
• Mentor and raise the technical capability of architects and engineers across Digital Quality, and lead architecture and AI Communities of Practice that spread good practice across DQ and the wider Digital organisation.
• Ensure architectural decisions embed GxP computerised system compliance, data integrity, data privacy and cyber security requirements from the outset, in partnership with Quality, Legal, Privacy, Compliance and Cyber Security.
• Define validation-efficient architecture patterns that allow AI and cloud capability to be deployed at pace while remaining inspection-ready.
• Exercise foresight in evaluating emerging AI, data and platform technologies, and act independently on self-initiated proofs of concept and technical experiments that challenge the status quo and shape the multi-year DQ technology roadmap.
• Take appropriate, well-governed technical risk, and translate proven experiments into scaled, supportable production capability with a defined adoption path.

Qualifications:

• Bachelor's degree required in Information Technology, Engineering, Environmental or Occupational Health Sciences, Life Sciences or a related discipline.
• 10+ years of relevant experience in solution architecture, technical leadership or enterprise software delivery (9+ years with an MS/MBA; 7+ years with a PhD), including demonstrable accountability for architecture across a portfolio of products rather than a single application.
• Demonstrated experience designing and delivering AI or machine learning capability into production, including an understanding of large language model and agentic architecture patterns, retrieval, evaluation and model lifecycle management.
• Strong experience with cloud, integration and data architecture, and with modern software engineering and Agile ways of working; SAFe preferred.
• Experience setting and driving adoption of technical standards across teams that do not report to the incumbent, influencing senior stakeholders through technical credibility.
• Experience across all stages of the system and application lifecycle, from value analysis and business case through deployment to value realisation and technical debt management.
• Strong written and verbal communication skills, with the ability to explain complex technical trade-offs to non-technical senior leaders.
• Experience in the pharmaceutical industry or another highly regulated manufacturing environment, including GxP computerised system validation and data integrity expectations (e.g. GAMP 5, 21 CFR Part 11, Annex 11).
• Working knowledge of quality or EHS business processes and the platforms that support them (for example Veeva, LIMS, Cority).
• Experience deploying AI in a regulated context, including responsible AI governance, model risk assessment and human-in-the-loop design.
• Recognised architecture certification (e.g. TOGAF) or equivalent cloud architecture certification.
• Track record of external technical contribution - industry forums, working groups, publications or conference speaking.

Physical/Mental Requirements:

Typical desk job; no specific physical or mental requirements beyond standard office work.

Non-Standard Work Schedule, Travel or Environment Requirements:

• Occasional travel up to 20%.
• Ability to work with colleagues virtually across different regions and time zones, including occasional out-of-hours working to support deployment go-lives and hypercare.

Organizational Relationships:

Reports into Digital, PGS, Enabling Functions & Enterprise Platforms - Digital Quality Lead.

Key internal relationships include Enterprise Architecture, the enterprise AI and Data organization, Cyber Security, Digital Compliance and Computerized System Validation, Legal and Privacy, and senior business stakeholders across Quality and EHS in Global Supply. External: strategic software and AI vendors, cloud and platform providers, implementation and managed service partners, and relevant industry and regulatory technical forums.

Resources Managed:

Financial Accountability:

No direct budget ownership. Influences the architecture and technology decisions underpinning the Digital Quality portfolio representing approximately $10M of annual software, services and transformation investment, and is accountable for identifying reuse and rationalization opportunities that improve return on that investment.

Supervision:

Individual contributor with no direct reports. Provides technical direction and design authority to matrixed teams of product, engineering, data, validation and vendor resources across the Digital Quality portfolio, and mentors architects and senior engineers across the organization.

Work Location Assignment:

Hybrid

Skills

AI, Agentic, Architecture, Cloud, Data, Integration, LLM, Large Language Model, Machine Learning, TOGAF