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 end‑to‑end 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 player‑coach role: the individual is expected to lead delivery while remaining technically hands‑on, especially in areas where deep technical judgement and collaboration with Digital are required.
Key Responsibilities
AICL Platform Leadership & Architecture (Core)
- Lead the design and hands‑on delivery of China AICL capabilities, including AI‑ready data products, semantic/consumption layers, and orchestration patterns.
- Translate AI and analytics use‑case needs (e.g. DDD) into concrete technical designs and deliverables, moving from PoC or concept into MVP and production‑ready solutions.
- Ensure AICL components are usable, stable, and reusable, not just architecturally sound.
Cross‑Functional Alignment (China + Global)
- Work day‑to‑day with Digital / engineering teams to co‑design 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
Bachelor’s, Master’s, 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 hands‑on with engineering or Digital teams to deliver production‑ready data or AI capabilities.
- Solid understanding of data engineering, consumption layers, and AI‑ready 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.
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