Director, AIT Acceleration Lead, Principal Engineer - CHN - SH - Shanghai - CITIC Office | Pfizer Job

1. Role Summary

The Director, AIT Acceleration Lead runs Forward Deployment Engineering (FDE) and AI engineering as one acceleration engine, working with AI Product Owners to move high-value ideas from opportunity framing to validated minimum viable products. The role turns prioritized AI demand into working, evaluated solutions at speed, then hands them to Digital & Technology (D&T) and PIC for production, scale and sustainable ownership.

The Acceleration Lead owns the rapid-validation and AI engineering capability within AITO, setting engineering and validation standards and leading a multidisciplinary engineering squad that works in PO-led PODs to prove feasibility, desirability and value before scale decisions. The FDE squad focuses on 0→1 business validation of flagship AI opportunities, rapid solution build, small language model development and the AI layer of the AI Empowered Consumption Layer (AICL).

2. Key Accountabilities

• Lead FDE and AI engineering as a single acceleration engine, moving prioritized AI portfolio ideas from opportunity framing through rapid prototype, proof of concept and validated MVP.

• Partner with AI Product Owners, who report to the AITO Head, on product vision, scope, backlog and MVP success criteria; own technical feasibility, solution build, evaluation evidence and scale-readiness for the prioritized AI portfolio.

• Work with AI Product Owners in PO-led PODs, providing FDE and engineering capacity per product and applying shared discovery, engineering and validation standards.

• Lead the AIT FDE squad for fast, hands-on solution design, prototyping and validation of flagship AI opportunities (0→1).

• Lead rapid AI solution development and implementation through the AI engineering team: agentic applications, RAG pipelines and system integrations built on the China AI platform, using its reference architectures and guardrails.

• Own small language model (SLM) and domain model development for priority commercial use cases – data preparation, fine-tuning, distillation and evaluation of open-weight models – deployed through the China LLM foundation and production release gate owned by the AI Platform & Foundation Director.

• Own the AI layer of AICL (agents, retrieval and orchestration), delivered as one POD with the D&T Data line (CL layer) and GCA DSM (business definitions and unified KPIs).

• Drive builder adoption with China business teams and engineers through sample agents, reusable components, documentation, training and an active user community, in step with GTMx AI fluency and enablement programs.

• Apply disciplined validation gates - feasibility, desirability and value - so only evidenced solutions progress to scale.

• Manage the acceleration-to-scale seam with D&T and PIC, ensuring validated solutions have clear ownership, production architecture, evaluation evidence, funding and transition plans.

• Partner with the AIT Value & Performance Lead to connect product decisions to business cases, KPIs and model-cost economics.

• Lead and develop a multidisciplinary engineering squad, balancing speed, quality and reuse.

3. Required Capabilities & Competencies

(a) Critical Capabilities & Qualifications

• Strong AI product sense and proven partnership with product owners: discovery-to-MVP practices, value-based prioritization and translating business problems into technical solutions.

• Forward deployment / applied AI engineering: agentic applications, retrieval, model and API integration, orchestration, prompt and context engineering, evaluation, guardrails and human-in-the-loop design.

• Hands-on experience building retrieval-augmented generation (RAG) pipelines – embeddings, re-rankers and vector databases – and writing production-quality Python.

• Small language model and domain model development: fine-tuning, distillation and evaluation of open-weight models on GPU infrastructure, including sizing and performance testing; experience on domestic accelerators is a plus.

• Rapid experimentation and validation at scale — moving ideas from concept to evaluated MVP with clear evidence.

• Working knowledge of MLOps and LLMOps, model-cost / cost-per-outcome discipline and enterprise deployment seams.

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a closely related discipline; an advanced technical or business degree is preferred.

• Typically 12+ years of progressive relevant experience, including several years leading AI engineering and applied AI teams at scope and complexity appropriate to the Director level. Candidates with an exceptional background may be considered from 10+ years.

• Demonstrated ability to work across business, product, technology, data and governance stakeholders in a matrixed environment.

• Experience in a highly regulated industry and the China technology, data, privacy or vendor environment is preferred.

(b) Leadership Expectations

• 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 peer teams and partners.

• Precision and learning: communicate clearly, documents decisions, uses evidence and telemetry and continuously improves delivery.

• Acceleration mindset: drives speed with rigor, making timely scale or stop calls on the evidence.

4. Key Collaboration

The Director partners with the AI Product Owners on product backlog and MVP scope, and with the AIT Value & Performance Lead on business cases and investment decisions and works within AITO under the AITO Head. The role builds on the China AI platform and LLM foundation owned by the AI Platform & Foundation Director, and delivers the AICL AI layer as one POD with D&T Data Engineering & Semantics (CL layer) and GCA DSM (business definitions and unified KPIs). It manages the acceleration-to-scale seam with Digital & Technology (D&T) and the Pfizer Innovation Center (PIC), directs matrixed PIC and contractor capacity, and partners with Go-to-Market Transformation (GTMx), Global Commercial Analytics (GCA) and China Business Units to shape demand and land solutions. It also works with International AIT product and engineering communities, and with Legal, Privacy, Risk, Compliance and Security stakeholders.

5. Measures of Success

• High-value ideas progressed from opportunity framing to validated, evaluated MVP with a named owner and clear scale or stop decision.

• Faster, higher-quality validation and a clean acceleration-to-scale transition to D&T and PIC.

• Strong engineering and validation discipline – clear standards and evaluation coverage, with close alignment with AI Product Owners on backlog and MVP criteria.

• Reuse of AI products and FDE patterns across the prioritized portfolio.

• AICL AI layer and priority small language models delivered on the China AI platform, with evaluation evidence and reuse across use cases.

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.