{"id":4187488,"url":"https://alion.io/job/astrazeneca-forward-deployed-engineer-china-rd-it","title":"Forward Deployed Engineer, China R&D IT","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":94,"open_postings":188,"ghost_share":0.011,"stale_share":0.356,"repost_share":0.112,"time_to_fill_p50_days":19,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Shanghai, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-11-12T18:41:57Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T18:41:57Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the role\nThe 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.\nOur 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.\nThe 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.\nKey responsibilities\nBuild proofs of concept\nBuild working POCs for AI, data and software ideas within an agreed time box, typically 2 to 6 weeks.\nAgree written success criteria with the product owner before building, and measure the POC against them.\nChoose the simplest approach that answers the question: configure, integrate, script or build.\nDemonstrate results to users and leaders, and give a clear recommendation: proceed, stop or iterate once more.\nWork directly with users\nSpend time with scientists, clinical, regulatory and lab staff to see how the work is really done.\nTurn loosely defined needs into something concrete that users can react to.\nCollect user feedback during the POC and adjust quickly.\nIntegrate across systems and stacks\nConnect vendor systems through APIs, data pipelines and automation.\nExplore and profile data early, so that data quality and access problems surface before production.\nWork across AWS and Alibaba Cloud, and across the different stacks used by our vendors.\nApply AI where it adds value\nBuild LLM applications, retrieval and agent workflows, and classic machine learning prototypes.\nEvaluate model quality, cost and failure modes, and report them honestly.\nUse only AI models and services approved for use in China.\nHand over to production vendors\nKnow the AZ guidelines for production workloads well enough that POC choices do not block the production build.\nDeliver a handover package for each approved POC: code, design notes, data findings, test results, known risks and open questions.\nBrief the vendor, support the first weeks of the production build, then step back.\nGive the business partner and PM a technical view on vendor scope, design and estimates, based on POC evidence.\nWork within guardrails\nBuild in the sandbox provided by the cloud team, using patterns approved by enterprise architecture.\nUse de-identified or synthetic data by default, and obtain approval before using personal, clinical or genetic data.\nKeep POCs separate from GxP-validated production systems, and never present a POC as a production service.\nKeep all code in the company repository with basic documentation, and retire POCs that are stopped.\nHow the role works with others\nThe FDE builds; the surrounding roles set direction, provide the platform and take the result into production.\nRole\nWhat they provide\nWhat the FDE gives back\nProduct owner\nThe problem, the users, priorities and success criteria\nA working POC and a proceed, stop or iterate recommendation\nTechnology Business Partner\nBusiness context, stakeholder access and the go or no-go decision\nTechnical evidence for roadmap and investment decisions\nPM\nIntake, time box, decision gate and handover process\nHonest progress, risks and effort estimates\nEnterprise architecture\nApproved patterns, stacks and AZ guidelines\nFeedback on where the patterns help or slow POC work\nCloud team\nSandbox environments on AWS and Alibaba Cloud\nClear infrastructure needs and findings on cost and performance\nVendors\nProduction build and support under AZ guidelines\nHandover package, briefing and early build support\nRequired qualifications\nExperience and education\nBachelor's degree or above in a STEM discipline, such as computer science, engineering, data science or life sciences.\n[6+] years of hands-on software or data engineering, with working products or prototypes delivered end to end.\nExperience working directly with business users or customers, not only from written requirements.\nEngineering breadth\nStrong coding skills in Python, plus SQL and at least one of JavaScript/TypeScript or Java.\nBack-end and API development, including integration with third-party and vendor systems.\nData engineering: pipelines, data modelling, data quality checks and working with messy source data.\nEnough front-end skill to give users a usable interface for a POC.\nSound engineering habits: version control, testing, documentation and security basics.\nAI and GenAI\nHas built LLM applications, such as retrieval-augmented generation or agent workflows, beyond a demo.\nUnderstands how models work, how to evaluate them, and where they fail.\nUses AI coding tools well to increase delivery speed.\nAWS and Alibaba Cloud\nHands-on experience building on AWS or Alibaba Cloud, and working knowledge of the other.\nFamiliar with core compute, storage, database, serverless, container and AI services on both.\nUnderstands how AWS China regions differ from AWS global regions.\nChina data and cybersecurity awareness\nWorking 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.\nKnows when to stop and ask: sensitive personal information, genetic data, cross-border transfer or use of an external AI service.\nCommunication\nExplains technical findings clearly to non-technical users and leaders.\nWrites handover documents that a vendor team can act on without follow-up.\nClear written and spoken Mandarin and English.\nPreferred qualifications\nMaster's degree or PhD in a STEM discipline.\nExperience in pharma, biotech, CRO or another regulated industry.\nExposure to clinical, laboratory, regulatory or bioinformatics systems and data.\nEarlier experience as a forward deployed engineer, solutions engineer or consultant-engineer in a client-facing role.\nExperience handing work over to vendor or outsourced delivery teams.\nExperience with locally available large language models and model services in China.\nPersonal attributes\nBuilder. Prefers showing a working result to presenting slides.\nFast and pragmatic. Picks the shortest route to an answer and accepts that POC code is not production code.\nComfortable with ambiguity. Starts from an unclear problem and shapes it with the users.\nCurious about the science. Wants to understand how clinical, regulatory and lab work is done.\nHonest. Says clearly when an idea does not work, and why.\nSelf-motivated. Manages several POCs without close supervision.\nGood at letting go. Hands over cleanly and moves to the next problem.\nFirst 30, 60 and 90 days\nThe 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.\nDays 1 to 30: Get set up and learn the landscape\nMeet users in each of the four domains and observe their day-to-day work.\nLearn the main vendor systems, their APIs and where the data sits.\nStudy the AZ guidelines for production workloads with enterprise architecture.\nGet sandbox environments, repository and tools working on AWS and Alibaba Cloud with the cloud team.\nAgree the POC process with the PM: intake, time box, decision gate and handover.\nStart one small POC from the existing backlog.\nOutput by day 30: working environments, a short map of systems and data sources, and the first POC under way.\nDays 31 to 60: Deliver the first POCs\nComplete the first POC, demonstrate it to users and give a recommendation.\nStart a second POC of a different type, so that AI, data and software are all tested early.\nBuild reusable starters, such as templates and connectors for common systems, to shorten later POCs.\nDraft the standard handover package with enterprise architecture and the PM.\nOutput by day 60: one POC completed with a clear decision, a second in progress, and an agreed handover template.\nDays 61 to 90: Prove the handover\nHand over the first approved POC to a vendor and support the start of the production build.\nComplete the second POC and start the third.\nReview what slowed the first POCs, and agree fixes with enterprise architecture, the cloud team and the PM.\nAgree a POC pipeline for the next two quarters with the product owner and the business partner.\nOutput by day 90: two POCs completed, one handed over to a vendor, a reusable toolkit in place, and an agreed pipeline of upcoming POCs.\nDate Posted\n12-11月-2026Closing Date\n30-12月-2026AstraZeneca 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. 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