Senior Associate, AI Platform & Foundation

Job description

1. Role Summary The Senior Associate is a hands-on platform engineering professional within AITO AI Platform & Foundation, contributing to the build, testing, operation and continuous improvement of China AI platform, cloud and LLM foundation capabilities. The role performs hands-on platform engineering under established architecture, security, privacy and service standards, and supports AI engineers and PODs building on the platform. 2. Key Accountabilities Deliver assigned AI platform, cloud, LLM foundation and platform-operations work to agreed acceptance criteria and quality standards. Contribute hands-on build, testing, documentation, monitoring and support for China AI platform capabilities — AI gateway, guardrails, agent runtime, AI development toolchain, reusable AI services, and separate innovation and production environments. Contribute hands-on build, testing, automation, documentation and support for approved China cloud environments, including Tencent Cloud and Alibaba Cloud, and other approved providers as applicable — environments, identity and access management, GPU capacity, model serving, monitoring and infrastructure as code. Support China LLM foundation activities — model onboarding, qualification, evaluation, version and lifecycle management — and prepare evidence for the AI platform technical qualification gate. Contribute to platform-level operations — monitoring, incident response, runbooks, service health, and model, token and cost reporting. Execute assigned model, service and tool onboarding activities, maintain qualification evidence and keep the platform inventory current. Create and maintain technical specifications, test evidence, runbooks and reusable knowledge assets, and escalate blockers promptly. Support builder adoption through sample agents, reusable components, documentation and training, and collaborate effectively with PIC and contractor resources. 3. Required Capabilities & Competencies AI platform and cloud engineering: model serving or Model as a Service (MaaS) integration, AI gateway, guardrails, agent frameworks, containers and Kubernetes, identity and access management, infrastructure as code and CI/CD. LLM foundation management: model qualification, pre-production evaluation (safety, content compliance and performance) and lifecycle management. Production service operation, observability, cloud cost control, security and privacy review, and working knowledge of PIPL, CAC generative AI rules and MLPS. Outcome ownership: connects decisions to measurable business, user, service and transformation outcomes. AI-first and enterprise-minded: applies responsible AI, reuse, architecture, security, privacy and scale principles appropriate to the work. POD collaboration: enables clear accountability, effective cross-functional execution and minimal handoff friction across the AI engineering pool, D&T, PIC, Corporate and Global CIO teams and governance partners. Precision and learning: communicates clearly, documents decisions, uses evidence and telemetry and continuously improves delivery. Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence or a related computing discipline. 5+ years of progressive relevant experience based on a bachelor’s degree, where a master’s or doctoral degree may reduce the years required. Demonstrated ability to work across business, product, technology, data and governance stakeholders in a matrixed environment. Experience in the pharmaceutical or another highly regulated industry, and the China technology, data, privacy or vendor environment, is preferred. 4. Key Collaboration Aligns the AI platform technical qualification gate with the production-readiness and go-live gate run by D&T Enterprise Engineering, Deployment & Operations, and collaborates with PIC and contractor resources. Outside CEI, it engages relevant Corporate and Global CIO platform, cloud hosting, cybersecurity and AI governance teams, Procurement, Legal, Compliance, the China Data Protection Office (DPO) and cloud vendors as needed. 5. Measures of Success Timely, high-quality delivery of assigned platform, cloud and LLM foundation work, supporting agreed service levels. Accurate, complete qualification evidence for models, services and tools entering the AI platform technical qualification gate. Reliable, well-tested and well-documented platform components, configurations and operational runbooks. Reusable platform services, components and knowledge assets adopted by AI engineers and builders across China Commercial. Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates. To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers. Information & Business Tech