This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.
Role Summary
AstraZeneca's scientists and clinicians increasingly work through agentic AI platforms and enterprise AI assistants. What decides whether those tools help is the layer around them: the skills people can use, the connectors that give them governed access to company data, and the way people get onboarded, supported and measured. We are seeking a Product Owner, Agentic Platform & Enterprise Interfaces, to own that layer for R&D.
You will be the day-to-day product owner of AISI's agentic platform for as long as it runs, and you will own the skills library, marketplace and data connectors that make AI assistants useful in R&D. You will make sure that access to data is governed, that costs and usage are visible, and that users are onboarded and supported well. As the platform landscape changes, you will keep the user experience and the governance consistent for the people who depend on it.
You will work with AI scientists and engineers, Enterprise AI platform and gateway teams, information security, architecture, data owners and external delivery partners. The role is hands-on and fast-moving. The ideal candidate is a product owner who is comfortable with technical detail, can say no clearly, and takes pride in how users experience the product.
Key Responsibilities
Platform and Interface Product Ownership
- Own the roadmap, backlog and release priorities for the R&D agentic platform and its interfaces, working with engineering leads on scope and sequencing.
- Decide what the platform should do itself, and what should move to enterprise assistants as they mature.
- Write clear product requirements, acceptance criteria and release notes, and keep decisions documented.
Skills, Marketplace and Connectors
- Own the skills library and marketplace: intake, quality review, publication, retirement and ownership of each skill.
- Own the connector roadmap, including connectors to enterprise data and systems built on open standards such as MCP. Prioritise requests against value, risk and engineering capacity.
- Set the criteria for approving connectors and skills, and work with security, architecture and data owners so that each one is reviewed before release.
Governed Data Access and Security
- Define with data owners and security how users get access to data through the platform, including approval flows and criteria for patient-related data.
- Make sure security findings are tracked to closure, for example weaknesses in how credentials, secrets or conversations are handled.
- Keep access, audit and retention requirements visible in the backlog and review them with Product Compliance.
User Onboarding, Access and Support
- Run the intake and onboarding process for new users and groups, including seat allocation and the weekly review of access requests.
- Work with support engineers and change partners on support queues, user communications and training.
- Handle requests from business areas in flight, and give each a clear answer and timeline.
Usage, Cost and Value
- Own the platform's usage and cost metrics, including model and token cost per session, and work with finance and platform teams on budgets and tiers.
- Define and report value measures and user stories that show what the platform delivers in R&D.
- Contribute to planning cycles with a clear view of run cost and options.
Cross-functional Collaboration
- Work closely with AI scientists, engineers, Enterprise AI gateway and platform teams, security, architecture and business sponsors.
- Communicate clearly about what is shipping, what is blocked and why, including when a request has to wait.
Desired Profile
Experience
- 5 or more years of experience as a product owner or product manager of a technical platform, data platform, API or developer-facing product.
- Experience with LLM-based applications or agentic systems, including how skills, tools and connectors are managed.
- Working knowledge of APIs, integration patterns and, ideally, the Model Context Protocol (MCP).
- Experience managing user access, onboarding and support for an enterprise tool.
- Understanding of information security and data access governance, ideally in a regulated environment.
- Experience tracking usage and cost for cloud or AI services, and making product decisions from the data.
- Proven ability to prioritise a long request queue and communicate trade-offs clearly.
Desirable
- Experience in pharmaceutical or life sciences R&D.
- Familiarity with FinOps practices for cloud or model usage.
- Working knowledge of Atlassian tools (JIRA, Confluence).
#EAI
Date Posted
26-oct-2026
Closing Date
08-nov-2026
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.
