{"id":3467837,"url":"https://alion.io/job/astrazeneca-it-product-owner-baic","title":"IT Product Owner, BAIC","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":91,"open_postings":127,"ghost_share":0.008,"stale_share":0.465,"repost_share":0.11,"time_to_fill_p50_days":20,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Product","role_family":"Product","seniority":"senior","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":{"min_usd":48000,"max_usd":107000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":4457},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"FinOps","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Platform Engineering","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"AI Agents","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Confluence","optional":true},{"name":"Function Calling","optional":true},{"name":"Git","optional":true},{"name":"Jira","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLMOps","optional":true},{"name":"Python","optional":true},{"name":"TypeScript","optional":true}],"status":"closed","first_seen_at":"2026-10-29T15:56:07Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-31T19:44:14Z","board_verified":false,"closed_at":"2026-10-31T19:44:14Z","days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"DUTIES & RESPONSIBILITES\n1. Product Vision, Strategy & Roadmap\nDefine and own the vision, strategy and roadmap for the agent platform, aligned with the BAIC platform strategy and AstraZeneca’s enterprise AI strategy \nTrack the fast-moving agent ecosystem (models, protocols, frameworks, open standards) and decide what to build, buy, adopt or retire; keep the platform modular so components can be swapped as technology changes \nTranslate the needs of agent developers and priority business use cases into an outcome-based roadmap with clear OKRs, release plans and success metrics \nCommunicate the roadmap and trade-offs clearly to BAIC leadership, developer communities and stakeholders \n2. Core Agent Capabilities: LLM Gateway, MCP & Memory\nLLM gateway: own a single, governed entry point to multiple models, with model catalog, routing and fallback, authentication, quotas and rate limits, caching, usage metering and chargeback, logging and content safety controls \n MCP & tools: own the MCP server registry and gateway, standards for building and certifying MCP servers, and secure identity-aware connectors to AZ systems and data (e.g. R&D data platforms, document and knowledge repositories, business applications) \n Memory & context: own short- and long-term agent memory, retrieval (RAG) and vector services, and context management, with clear controls for data access, retention and privacy \nShape adjacent capabilities as the platform matures: agent runtime and orchestration, agent identity and permissions, agent-to-agent communication, prompt and configuration management \n3. Developer Experience & Adoption\nTreat agent developers as customers: run discovery and feedback loops, and measure developer satisfaction and time-to-first-agent \nDeliver a strong self-service developer experience: SDKs and APIs, reference architectures, templates, documentation, sandbox environments and simple onboarding \nBuild an internal developer community through demos, office hours, training and champions; drive reuse of shared MCP servers, memory and patterns across teams \nTrack platform adoption (teams, agents in production, API and token usage) and use the data to prioritise the backlog \n4. Trust, Security & Governance by Design\nEmbed guardrails in the platform: prompt-injection and data-leakage protection, PII handling, content filtering, tool permission scoping and human-in-the-loop controls \nProvide observability and evaluation: end-to-end tracing of agent and tool calls, audit trails, quality and safety evaluations, and production monitoring \nMake Responsible AI and compliance easy to follow: align with AZ AI governance, information security and privacy policies, support CSV/GxP needs for agents used in regulated processes, and meet China requirements on generative AI services, data security and cross-border data transfer \nWork with security and architecture teams on threat modelling, access reviews and risk acceptance for new capabilities \n5. Backlog Ownership & Roadmap Execution\nOwn and prioritise the product backlog; write epics, user stories and acceptance criteria, including non-functional requirements (latency, availability, scalability, security) \nLead Agile delivery with platform engineering squads and vendors/OSPs; make timely scope and release decisions and deliver the roadmap predictably \nDefine and manage platform SLAs/SLOs, versioning and deprecation policies, and backward-compatibility commitments to developers \nManage dependencies with cloud, identity, network, data and security teams; manage risks, issues and release readiness \n6. Value, Cost & Vendor Management\nOwn the platform budget (annual plan, MTP inputs, forecasting, variance analysis), including model and token spend \nApply FinOps for AI: monitor token and compute consumption, optimise model routing and caching, and give consuming teams cost transparency and showback/chargeback \nTrack and report platform value (e.g. development time saved, reuse, cost per agent, reliability) to BAIC leadership \nManage model provider and vendor relationships, including commercial evaluation, contracts, SLAs and performance reviews \nJOB REQUIREMENTS & COMPETENCIES\nJob requirements\nAcademic / Professional qualification\nBachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science or a related field (essential) \nMaster’s degree in Computer Science, Artificial Intelligence or a related field (desirable) \nTechnical / skill training\nProduct ownership certification (e.g. CSPO, PSPO, SAFe POPM) preferred \nCloud certification (e.g. AWS or Azure) and recent training in LLMs, agentic AI or AI security is a plus \nWorking experience\n6+ years in software engineering, platform engineering or technical product roles, including 2+ years as product owner or product manager for a developer-facing platform, API or cloud service, and hands-on experience with LLM and agent technologies \nEssential\nProven experience owning the roadmap and backlog of a technical platform used by developers, and delivering it with Agile methods \nHands-on understanding of LLM and agent architecture: LLM gateways and model routing, tool calling and MCP, RAG and vector databases, agent memory, and agent frameworks \nSolid knowledge of cloud-native platforms (e.g. AWS/Azure, containers, Kubernetes), API design, identity and access management (OAuth/OIDC) and observability \nUnderstanding of AI security and risk (prompt injection, data leakage, tool misuse) and how to mitigate them at platform level \nExperience defining SLAs/SLOs, usage metrics and cost models for shared services \nAble to engage credibly with engineers, and translate technical capabilities into business value for leadership \nHigh autonomy and ownership; comfortable making decisions in a fast-changing technology landscape \nDesirable\nExperience in pharma, life sciences or other regulated industries, and awareness of how agents can support R&D use cases \nHas personally built and deployed agents or MCP servers; contributions to open-source agent or developer tooling \nExperience with LLM evaluation, LLMOps and agent observability tools \nFamiliarity with the model ecosystem available in China and with China regulations on generative AI services and data security \nExperience with FinOps for AI workloads and with commercial negotiations with model providers \nExperience in global, matrixed organisations \nLanguage ability\nFluent English and Mandarin Chinese, written and spoken; able to present to senior international audiences \nComputer literacy\nAble to read and write code (e.g. Python or TypeScript) to prototype, review APIs and test platform capabilities \nProficient with Agile and product tools (e.g. Jira, Confluence), developer tools (e.g. Git, API clients) and everyday GenAI and coding assistants \nOthers\nKey competencies:\nPlatform and developer-experience product mindset \nTechnical depth and systems thinking \nClear prioritisation and decision-making under uncertainty \nExecution focus and delivery discipline \nStakeholder management and influencing without authority \nCuriosity and continuous learning in AI \nLives AstraZeneca’s values \nDate Posted\n29-10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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