Forward Deployed Engineer, China R&D IT

About the role The Forward Deployed Engineer builds working proofs of concept (POCs) in weeks, so that the decision to invest in a production build rests on evidence. You sit with business users in R&D China, understand the problem first-hand, and build something they can try. Our landscape has several vendor systems, different IT stacks and many small new projects. Vendors build production workloads following AZ guidelines. Your job comes before that: prove quickly whether an idea works, what it takes, and what the risks are, then hand over cleanly to the vendor. The scope is AI, data and software alike. One month you may connect two vendor systems, the next build a data pipeline or an LLM application. You are supported by enterprise architecture for standards, the cloud team for infrastructure, PMs for process and product owners for direction. Key responsibilities Build proofs of concept • Build working POCs for AI, data and software ideas within an agreed time box, typically 2 to 6 weeks. • Agree written success criteria with the product owner before building, and measure the POC against them. • Choose the simplest approach that answers the question: configure, integrate, script or build. • Demonstrate results to users and leaders, and give a clear recommendation: proceed, stop or iterate once more. Work directly with users • Spend time with scientists, clinical, regulatory and lab staff to see how the work is really done. • Turn loosely defined needs into something concrete that users can react to. • Collect user feedback during the POC and adjust quickly. Integrate across systems and stacks • Connect vendor systems through APIs, data pipelines and automation. • Explore and profile data early, so that data quality and access problems surface before production. • Work across AWS and Alibaba Cloud, and across the different stacks used by our vendors. Apply AI where it adds value • Build LLM applications, retrieval and agent workflows, and classic machine learning prototypes. • Evaluate model quality, cost and failure modes, and report them honestly. • Use only AI models and services approved for use in China. Hand over to production vendors • Know the AZ guidelines for production workloads well enough that POC choices do not block the production build. • Deliver a handover package for each approved POC: code, design notes, data findings, test results, known risks and open questions. • Brief the vendor, support the first weeks of the production build, then step back. • Give the business partner and PM a technical view on vendor scope, design and estimates, based on POC evidence. Work within guardrails • Build in the sandbox provided by the cloud team, using patterns approved by enterprise architecture. • Use de-identified or synthetic data by default, and obtain approval before using personal, clinical or genetic data. • Keep POCs separate from GxP-validated production systems, and never present a POC as a production service. • Keep all code in the company repository with basic documentation, and retire POCs that are stopped. How the role works with others The FDE builds; the surrounding roles set direction, provide the platform and take the result into production. Role What they provide What the FDE gives back Product owner The problem, the users, priorities and success criteria A working POC and a proceed, stop or iterate recommendation Technology Business Partner Business context, stakeholder access and the go or no-go decision Technical evidence for roadmap and investment decisions PM Intake, time box, decision gate and handover process Honest progress, risks and effort estimates Enterprise architecture Approved patterns, stacks and AZ guidelines Feedback on where the patterns help or slow POC work Cloud team Sandbox environments on AWS and Alibaba Cloud Clear infrastructure needs and findings on cost and performance Vendors Production build and support under AZ guidelines Handover package, briefing and early build support Required qualifications Experience and education • Bachelor's degree or above in a STEM discipline, such as computer science, engineering, data science or life sciences. • [6+] years of hands-on software or data engineering, with working products or prototypes delivered end to end. • Experience working directly with business users or customers, not only from written requirements. Engineering breadth • Strong coding skills in Python, plus SQL and at least one of JavaScript/TypeScript or Java. • Back-end and API development, including integration with third-party and vendor systems. • Data engineering: pipelines, data modelling, data quality checks and working with messy source data. • Enough front-end skill to give users a usable interface for a POC. • Sound engineering habits: version control, testing, documentation and security basics. AI and GenAI • Has built LLM applications, such as retrieval-augmented generation or agent workflows, beyond a demo. • Understands how models work, how to evaluate them, and where they fail. • Uses AI coding tools well to increase delivery speed. AWS and Alibaba Cloud • Hands-on experience building on AWS or Alibaba Cloud, and working knowledge of the other. • Familiar with core compute, storage, database, serverless, container and AI services on both. • Understands how AWS China regions differ from AWS global regions. China data and cybersecurity awareness • Working knowledge of what the Personal Information Protection Law, Data Security Law, Cybersecurity Law and human genetic resources regulations mean for how data is accessed, stored and moved. • Knows when to stop and ask: sensitive personal information, genetic data, cross-border transfer or use of an external AI service. Communication • Explains technical findings clearly to non-technical users and leaders. • Writes handover documents that a vendor team can act on without follow-up. • Clear written and spoken Mandarin and E