(Sr)Manager, Data & AI Engineer

Role Summary

The Senior Manager Data & AI Engineer is a hands-on engineering leadership role responsible for building and scaling data & AI engineering capabilities that enable AIT (AI Transformation) programs across international and local projects.

As the AICL Platform Lead you will play a pivotal role driving endtoend platform capability design (L4) coordinating with Data Science Digital analytics/reporting and global teams to translate use case needs into reusable platform assets. You will also act as the China coordination point for data governance forums to ensure decisions support trusted compliant data consumption & AI adoption.

You will design and deliver production-grade data pipelines semantic/consumption layers model/LLM serving services and operational tooling with strong engineering discipline (testing CI/CD observability security/compliance) with principles aligned with International data strategy as applicable

This is a playercoach role: the individual is expected to lead delivery while remaining technically handson especially in areas where deep technical judgement and collaboration with Digital are required.

Key Responsibilities

• AICL Platform Leadership & Architecture (Core)

  • Lead the design and handson delivery of China AICL capabilities including AIready data products semantic/consumption layers and orchestration patterns.

  • Translate AI and analytics usecase needs (e.g. DDD) into concrete technical designs and deliverables moving from PoC or concept into MVP and productionready solutions.

  • Ensure AICL components are usable stable and reusable not just architecturally sound.

• CrossFunctional Alignment (China Global)

  • Work daytoday with Digital / engineering teams to codesign and implement AICL solutions including integration deployment testing and operational handover.

  • Partner closely with Data Science/Digital teams and analytics stakeholders to ensure platform capability aligns with real use cases and adoption pathways.

  • Keep continuous bidirectional alignment with global counterparts on AI and data platform product evolution ensuring that global updates upgrades and lessons learned are shared proactively and that China requirements and constraints are fed back into global discussions.

• AI Consumption Enablement: Semantic/Context & Orchestration

  • Drive the enablement of semantic and context layers (e.g. mapping business terms to technical objects SSOT KPI integration organization/context enrichment) as foundational AICL capabilities.

  • Guide the design of AI consumption and orchestration patterns (e.g. structured unstructured retrieval coordination agent orchestration considerations) as part of AICL L4 capability planning.

• Data Quality & Observability:

  • Responsible for data quality and observability across AICL and related platforms ensuring enterprise standards and AI/analytics criteria are clearly defined and consistently followed by designated teams.

• AI/ML Engineering MLOps & LLMOps

  • Productionize ML/AI solutions: build training/inference pipelines packaging deployment monitoring and lifecycle management for models and AI services

  • LLM Development: Proven experience in designing and developing LLM-based applications including RAG systems and AI Agents.

  • Framework Proficiency: Hands-on experience with popular LLM orchestration frameworks such as LangChain LlamaIndex or similar tools for building scalable AI solutions.

  • Implement LLMOps practices (prompt testing runtime monitoring evaluation/guardrails hallucination control patterns) for agentic and LLM-enabled solutions where applicable.

  • Collaborate with Data Scientists to integrate models into scalable services and workflows enabling repeatable delivery and BAU operations.

• Backend Services & Platform Engineering

  • Build and maintain Python services and REST/gRPC APIs (e.g. FastAPI) that support inference workflows metadata/services internal platforms and automation.

  • Establish clear API contracts data models validation and secure authN/authZ patterns for enterprise integration.

Required Qualifications

• Bachelors Masters or PhD in Computer Science Statistics Data Science Engineering or a related quantitative field.

• 8 years in data/analytics platforms dataproduct or AI enablement roles with experience leading cross-functional delivery and platform adoption

• Strong understanding of end-to-end data & AI platform architecture especially consumption/semantic enablement and platform lifecycle thinking.

• Strong programming skills in Python; experience building backend services with FastAPI/Flask and designing REST APIs.

• Hands-on experience developing ETL pipelines using Python SQL working with databases and data modeling concepts.

• Familiar with modern engineering practices (version control CI/CD concepts testing observability) sufficient to lead industrialized delivery with Digital teams.

• Able to evaluate technical options and make pragmatic decisions under China constraints

• Data Quality & Observability: Experience implementing and managing frameworks for data quality testing observability and alerting.

• Strong stakeholder management and communication skills (China global) able to maintain long-term alignment on platform evolution

• Experience with cloud platforms and operating services in cloud environments (AWS/Azure/GCP); familiarity with containers (Docker) and CI/CD.

• Strong engineering discipline: testing (unit/integration) code quality documentation and operational readiness.

Preferred:

• Strong execution capability in data / analytics / AI platform delivery.

• Proven experience working handson with engineering or Digital teams to deliver productionready data or AI capabilities.

• Solid understanding of data engineering consumption layers and AIready data enablement.

• Ability to translate business or AI needs into implementable technical solutions.

• Experience with AI consumption layer semantic layer/metadata enablement or orchestration/agent patterns

• Understanding of RAG / unstructured data enablement considerations and the implications for semantic/context layers

• Experience in regulated/data-sensitive environments and governance forums (decision framing standards setting).

• Familiarity with enterprise governance models and data lifecycle decision processes.

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.