Forward Deployment Manager
Pfizer is hiring a Forward Deployment Manager in Shanghai, China. Level rates it ; you can apply on Pfizer.
Level's read on this job
- On Level's scale, this forward deployed engineer role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions RAG, fine tuning and MLOps among its tools and skills.
- Level classes it as a senior role.
- Pfizer has 705 other open roles on Level.
- Posted in the last few days.
AI in this role
1. Role Summary
The Forward Deployment Engineer (FDE) owns the how — building working AI solutions fast. Within the AITO AI engineering pool, the role turns prioritized ideas and product requirements into hands-on prototypes, proofs of concept and validated minimum viable products, proving technical feasibility for flagship AI opportunities (0→1).
Working side by side with the AI Product Owner in a PO-led POD, the engineer designs, builds, integrates and evaluates AI and agentic solutions on the China AI platform, contributes to small language models and the AI layer of the AI Empowered Consumption Layer (AICL), and hands validated work to Digital & Technology (D&T) and PIC for production, scale and sustainable ownership.
2. Key Accountabilities
- Design, build and integrate AI and agentic solutions — rapid prototypes, proofs of concept and validated MVPs — for high-value opportunities.
- Engineer agentic applications and RAG pipelines: retrieval, model and API integration, orchestration, prompt and context engineering, and human-in-the-loop workflows, using China AI platform reference architectures and guardrails.
- Contribute to small language model and domain model development — data preparation, fine-tuning, distillation and evaluation — deployed through the China LLM foundation and AI platform release gate.
- Build the AICL AI layer (agents, retrieval and orchestration) as one POD with the D&T Data Engineering & Semantics team (CL layer) and GCA DSM (business definitions and unified KPIs).
- Build evaluation, testing and guardrails to prove feasibility, quality and safety before scale.
- Partner daily with the AI Product Owner to translate requirements into working solutions and iterate on feedback; surface constraints, effort and risk early to inform scale or stop decisions.
- Prepare clean, documented handovers to D&T and PIC, including architecture notes, evaluation evidence and known limitations.
- Apply model-cost / cost-per-outcome awareness in design and model choices.
- Contribute reusable patterns, components and sample agents to the China AI platform and builder community.
3. Required Capabilities & Competencies
- Hands-on AI / software engineering: rapid prototyping, applied development and integration of AI and agentic solutions; production-quality Python.
- Agentic and RAG engineering: embeddings, re-rankers, vector databases, model and API integration, orchestration, prompt and context engineering, evaluation and guardrails.
- Working knowledge of small language model fine-tuning and evaluation, MLOps and LLMOps, CI/CD and secure, privacy-aware deployment.
- Model-cost / cost-per-outcome awareness and pragmatic engineering trade-offs.
- Builder mindset: ships working solutions fast with rigor, curiosity and pragmatic engineering judgment, with strong cross-functional collaboration.
- Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence, Data Science or a related discipline.
- 8+ years of relevant technology experience, including 5+ years of hands-on software, data or AI engineering experience and demonstrated delivery of AI/ML or agentic solutions.
- Ability to work across business, product, technology, data and governance stakeholders in a matrixed environment.
4. Key Collaboration
The role works in the AITO AI engineering pool under the AIT Acceleration Lead, partnering daily with the AI Product Owner in a PO-led POD. It builds on the China AI platform and LLM foundation provided by AI Platform & Foundation, delivers the AICL AI layer with D&T Data Engineering & Semantics and GCA DSM, and prepares the acceleration-to-scale handover with D&T Enterprise Engineering, Deployment & Operations and the Pfizer Innovation Center (PIC). It also engages relevant China Business Units and, where useful, International AIT engineering communities.
5. Measures of Success
- High-value ideas turned into working, evaluated prototypes and MVPs at speed.
- Technical feasibility, quality and safety proven through evaluation and guardrails before scale.
- Clean, well-documented handovers that accelerate production engineering by D&T and PIC.
Reusable patterns, components and AICL AI-layer assets adopted across us
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
How we rate this
Forward Deployment Manager at Pfizer rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in forward deployed engineer jobs at this level
Pay
- Typical forward deployed engineer job
- $180k
This posting doesn't list pay. Use the typical forward deployed engineer figure when pay comes up. See forward deployed engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG, Fine-tuning, and ML Ops. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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