Staff, AI Product Manager at GSK in Bengaluru Luxor North Tower

Job description

About the Role: GSK’s Enterprise AI team, a highly skilled applied AI group, translates the promise of AI into measurable impact across every major group at GSK — R&D, Medical, Commercial, Supply Chain, HR, Legal, Finance, and beyond. Bionic pods — AI product managers, forward deployed engineers, full-stack engineers, data engineers, and AI agents and workers — embed directly with scientific and business teams to build AI products that change outcomes. We are hiring a Staff, AI Product Manager to own the Orchestration and Automation platform tower: the low and medium code platforms where GSK builds agents and automations, and the enablement model that lets thousands of non-engineers use them safely. The estate spans workflow and agent builders such as Power Platform, Copilot Studio and n8n, alongside the adjacent orchestration tooling that sits with them. You will own the roadmap and the governance model, and act as the platform consultant business teams come to when they need to turn demand into working automations. Why This Role Exists GSK runs various low and medium code platforms, and demand from business teams outpaces any central delivery team. This role exists to take consolidation decisions, and to build the self-enablement layer — guardrails, templates, training, support — that lets citizen developers build safely without engineering review on every change. In this role you will be the person who: Owns the Orchestration and Automation platform tower and its roadmap. Takes consolidation decisions across the low and medium code platform estate. Acts as the platform consultant for business teams building on the estate. Owns the governance model that defines what a citizen developer may build unsupervised. How This Differs from Traditional Product Management This is not a traditional enterprise product role managing a source system’s roadmap, releases, and backlog. As an AI Product Manager in Enterprise AI, you: Build new AI products from ambiguous, unsolved business problems. Work with probabilistic systems, defining evaluations, acceptable error, and human oversight. Help lead a bionic pod, deciding which work goes to engineers and which to AI agents and workers, and how they hand off. Prototype hands on and are measured on business outcomes. Key Responsibilities Platform Portfolio & Consolidation Map capability against actual usage across the whole low and medium code estate, and recommend what consolidates, what stays, and what retires. Quantify value rigorously. Build credible business cases that survive finance and senior business unit scrutiny — cycle time saved, manual effort removed, licence cost avoided, automations running in production. Develop deep automation pattern recognition so you can spot the workflows worth automating and the ones that should be fixed at source instead. Strategic Prioritization & Portfolio Shaping Shape the platform roadmap. Maintain a living view of in-flight, queued, and de-prioritized platform work, with the rationale for each decision, and feed it into the broader AI Platforms & Tools portfolio. Make trade-off recommendations. When two business sponsors both want platform investment, define the success metrics, run the analysis, and recommend where effort goes. Protect engineering focus. Push back — with evidence — on opportunities that won’t move scalable outcomes. Engineering Partnership & Enablement Consult on platform fit and design. Advise business teams and delivery squads on which platform fits, how to structure the build, and what it takes to reach production. Translate ambiguous requests into crisp problem statements and success metrics. Shape engagements end-to-end. Define scope, success criteria, exit conditions, and scaling pathways before kick-off, then stay close as engineers execute. Surface re-use and platform leverage when a one-off solution should become a reusable capability. Design the human and agent split. Decide which tasks the pod’s AI agents and workers own