Senior Manager, AI Engineering

Job description

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 Purpose Breakthroughs that change patients' lives... At Pfizer we are a patient centric company, guided by our four values: courage, joy, equity and excellence. Our breakthrough culture lends itself to our dedication to transforming millions of lives. Digital Transformation Strategy One bold way we are achieving our purpose is through our company wide digital transformation strategy. We are leading the way in adopting new data, modelling and automated solutions to further digitize and accelerate drug discovery and development with the aim of enhancing health outcomes and the patient experience. Flexibility We aim to create a trusting, flexible workplace culture which encourages employees to achieve work life harmony, attracts talent and enables everyone to be their best working self. Let’s