Senior Manager, AI Engineering

This is an opportunity to play a key role in building next-generation capabilities that support Pfizer's Global Clinical Supply Organization (GCS) and accelerate its digital transformation journey.

The Senior Manager, AI Engineering is a global technical leadership role responsible for designing, delivering, and scaling AI-powered products, data platforms, analytics solutions, and digital capabilities that enhance decision-making, operational effectiveness, and digital transformation across GCS.

Partnering with business, Digital, Product, Data Science, and technology teams worldwide, the role translates complex clinical supply challenges into secure, scalable, and business-impacting solutions. The position combines strategic technical leadership with practical engineering depth, requiring the ability to architect, prototype, guide development, and operationalize AI-enabled solutions while shaping solution architecture, engineering approaches, and delivery execution.

Operating with a high degree of independence, this role helps advance the adoption of AI and data-driven capabilities by transforming innovative concepts into production-ready solutions that deliver measurable business value.

This role reports directly to the Global Clinical Supply Central Operations Network Strategic Hub and AI/Data Analytics & Reporting Lead.

Role Responsibilities

• Lead the technical delivery of priority AI, data engineering, analytics, automation, and digital product capabilities for Global Clinical Supply (GCS) AI, Data Analytics & Reporting, ensuring solutions are scalable, secure, supportable, and aligned with business priorities.

• Translate complex business opportunities and operational pain points into pragmatic AI and data solution approaches, applying sound judgment to balance value, feasibility, risk, compliance, sustainability, and adoption.

• Provide technical leadership across cross-functional delivery teams, guiding solution design, engineering practices, delivery execution, and product-quality outcomes through expertise, influence, and matrix leadership.

• Establish and promote reusable engineering patterns, architecture approaches, and delivery standards for AI-enabled solutions, including data pipelines, cloud-native development, analytics platforms, AI lifecycle management, MLOps/LLMOps, Retrieval-Augmented Generation, and agent-based capabilities.

• Drive the transition of AI, analytics, and automation solutions from experimentation or prototype stages into production-ready, maintainable, monitored, and operationally sustainable capabilities.

• Partner with GCS, Digital, product, analytics, data science, and enabling-function stakeholders to align priorities, manage dependencies, shape delivery sequencing, and support adoption of AI and analytics capabilities.

• Evaluate technical trade-offs and dependencies across data availability, integration, platform constraints, quality, security, Responsible AI considerations, scalability, and user impact, making recommendations that support mid-term business and technology objectives.

• Communicate complex technical concepts, risks, options, and recommendations clearly to business, technical, and leadership audiences to support informed decision-making and effective execution.

QUALIFICATIONS

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Artificial Intelligence, or a related discipline; equivalent practical experience may be considered where appropriate.

• 7+ years of experience leading and delivering AI engineering, software engineering, data engineering, advanced analytics, automation, or digital product solutions within complex enterprise environments.

• Proven track record of leading or co-leading complex technology initiatives across cross-functional teams and delivering measurable business outcomes.

• Strong knowledge of AI solution architecture and implementation, including AI engineering, machine learning, predictive and prescriptive analytics, data engineering, cloud-native technologies, software engineering practices, and product-oriented delivery models.

• Expertise in designing, guiding, implementing, and operationalizing enterprise AI-enabled solutions, including machine learning, predictive analytics, prescriptive analytics, and generative AI capabilities, from business need through production-ready, maintainable, and scalable deployment.

• Demonstrated application of engineering best practices supporting AI lifecycle management, governance, monitoring, validation, security, explainability, scalability, and operational sustainability.

• Established ability to translate business challenges and opportunities into scalable technical solutions, solution architectures, implementation roadmaps, and delivery approaches.

• Ability to operate effectively in ambiguous and rapidly evolving environments, evaluate technical trade-offs, and recommend practical solutions that balance business value, risk, feasibility, and long-term sustainability.

• Strong communication, stakeholder engagement, and influencing skills, with the ability to explain complex technical concepts and recommendations to business, technical, and leadership audiences.

Preferred Qualifications

• Hands-on expertise or technical leadership in designing and delivering generative AI solutions utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, vector databases, orchestration frameworks, MLOps, LLMOps, DevOps, CI/CD, or related engineering practices.

• Knowledge of AI architecture frameworks, reusable engineering patterns, reference architectures, technology standards, and scalable delivery approaches that accelerate enterprise AI adoption.

• Proficiency with modern AI and data platforms such as Snowflake, Databricks, Dataiku, vector databases, cloud-native AI services, or comparable enterprise technology ecosystems.

• Ability to integrate AI capabilities into enterprise applications, digital workflows, analytics products, and operational business processes to drive measurable business value.

• Familiarity with AI orchestration frameworks, evaluation approaches, human-in-the-loop workflows, observability practices, and AI quality monitoring techniques that support trusted enterprise deployment.

• Experience applying Responsible AI, model governance, validation, privacy, security, and risk-management practices within regulated enterprise environments.

• Demonstrated ability to influence technical direction, shape architecture decisions, and provide technology leadership across complex cross-functional initiatives, products, and portfolios.

• Industry knowledge in pharmaceutical, life sciences, clinical supply, supply chain, or other regulated business environments

ORGANIZATIONAL RELATIONSHIPS
This role collaborates with Global Clinical Supply colleagues, Digital partners, and cross-functional stakeholders to design, deliver, and scale AI-enabled solutions. The role may also engage external technology partners and contingent resources supporting approved initiatives.

 
Work Location Assignment: Hybrid

Please apply by sending your CV and a motivational letter in English

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