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 English.
Preferred qualifications
Master's degree or PhD in a STEM discipline.
Experience in pharma, biotech, CRO or another regulated industry.
Exposure to clinical, laboratory, regulatory or bioinformatics systems and data.
Earlier experience as a forward deployed engineer, solutions engineer or consultant-engineer in a client-facing role.
Experience handing work over to vendor or outsourced delivery teams.
Experience with locally available large language models and model services in China.
Personal attributes
Builder. Prefers showing a working result to presenting slides.
Fast and pragmatic. Picks the shortest route to an answer and accepts that POC code is not production code.
Comfortable with ambiguity. Starts from an unclear problem and shapes it with the users.
Curious about the science. Wants to understand how clinical, regulatory and lab work is done.
Honest. Says clearly when an idea does not work, and why.
Self-motivated. Manages several POCs without close supervision.
Good at letting go. Hands over cleanly and moves to the next problem.
First 30, 60 and 90 days
The first 90 days should end with a POC handed over to a vendor, so the full path from idea to production handover has been proven once.
Days 1 to 30: Get set up and learn the landscape
Meet users in each of the four domains and observe their day-to-day work.
Learn the main vendor systems, their APIs and where the data sits.
Study the AZ guidelines for production workloads with enterprise architecture.
Get sandbox environments, repository and tools working on AWS and Alibaba Cloud with the cloud team.
Agree the POC process with the PM: intake, time box, decision gate and handover.
Start one small POC from the existing backlog.
Output by day 30: working environments, a short map of systems and data sources, and the first POC under way.
Days 31 to 60: Deliver the first POCs
Complete the first POC, demonstrate it to users and give a recommendation.
Start a second POC of a different type, so that AI, data and software are all tested early.
Build reusable starters, such as templates and connectors for common systems, to shorten later POCs.
Draft the standard handover package with enterprise architecture and the PM.
Output by day 60: one POC completed with a clear decision, a second in progress, and an agreed handover template.
Days 61 to 90: Prove the handover
Hand over the first approved POC to a vendor and support the start of the production build.
Complete the second POC and start the third.
Review what slowed the first POCs, and agree fixes with enterprise architecture, the cloud team and the PM.
Agree a POC pipeline for the next two quarters with the product owner and the business partner.
Output by day 90: two POCs completed, one handed over to a vendor, a reusable toolkit in place, and an agreed pipeline of upcoming POCs.
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
