Senior, AI Product Manager (Tech Transfer & Strategy)

GSK; United Kingdom; On-site; GBP 120,000 - 170,000; Full time

Job summary

GSK Enterprise AI is seeking a Senior AI Product Manager (Tech Transfer & Strategy) to shape how AI transforms GSK's manufacturing and supply network. You will partner with business stakeholders to identify high-impact opportunities, size value, and drive end-to-end AI product delivery with forward deployed engineers.

In this role, you will own tech transfer strategy, architecture, and agentification, aligning with sites, CMOs, and MSAT, to accelerate launches and optimize NPIs across global

Responsibilities

• Opportunity Identification & Value Sizing: Surface and size AI opportunities across your assigned engagements and adjacent business units; produce one-page opportunity briefs that are easy to compare and prioritize.
• Strategic Prioritization & Portfolio Shaping: Recommend what to start, continue, and stop across your engagements based on evidence, not enthusiasm.
• Tech Transfer Strategy & Agentification: Define and drive the New Product Introduction (NPI) launch strategy optimisation - identify bottlenecks and levers that shorten time from late-stage development to commercial supply; lead architecture definition for AI-enabled tech transfer.
• Engineering Partnership & Enablement: Be the day-to-day strategic partner to FDE pods; convert ambiguity into crisp problem statements, success metrics, and decision frameworks for engineers to act on.

Job description

Senior, AI Product Manager (Tech Transfer & Strategy )

About GSK Enterprise AI

Enterprise AI is GSK's centre of gravity for applied artificial intelligence. We pair forward deployed engineers and full-stack engineers with scientific, clinical, and business teams to build AI products that get used - not pilots that get archived. We work fast, ship in production, and measure ourselves on outcomes that matter to patients.

GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. We are committed to building a diverse and inclusive team - we welcome candidates from all backgrounds and underrepresented groups in technology, science, and pharma.

Position Summary

GSK's Enterprise AI team translates the promise of AI into measurable impact across drug discovery, development, manufacturing, and commercial. Forward deployed teams embed directly with scientific and business teams to build AI products that change outcomes.

This specific role sites in the Chemical Manufacturing & Control (CMC) area. This is the GSK function that turns a validated molecule into a reliably manufactured, quality‑assured medicine; its mission is to get safe, effective, consistently manufactured medicines to patients, faster. Its current mission is to double New Product Introductions (NPIs).

As a Senior AI Product Manager, you'll help shape how AI transforms GSK's manufacturing sites around the world: identifying where it will deliver the greatest value and keeping our teams always focused on the highest-impact opportunities. You'll partner closely with business stakeholders to prioritize opportunities based on feasibility and value. You'll take a complex business problem, structure it, size the value, prototype hands‑on, and turn it into something a forward deployed engineer (FDE) can build against.

You will run the day‑to‑day tech transfer engagement with sites and Contract Manufacturing Organizations (CMOs), and redesign processes at a detailed level. You will shape the strategy to optimise NPI launches, covering site impact assessment, capacity management, digital twins, scenario planning and virtual commissioning of lines, with the goal of one‑click deployment of a new line and product. You will define the target architecture, own the tech transfer package, and drive the AI‑native end‑to‑end process redesign.

Key Responsibilities

• Opportunity Identification & Value Sizing
  Surface and size AI opportunities across your assigned engagements and adjacent business units; produce one‑page opportunity briefs that are easy to compare and prioritize.
  Build credible value cases with finance‑grade reasoning behind cycle time saved, decisions accelerated, revenue uplift, or cost avoided.
  Develop hands‑on domain knowledge in your engagement areas - workflows, data, decisions, and the people who own them.
• Strategic Prioritization & Portfolio Shaping
  Recommend what to start, continue, and stop across your engagements based on evidence, not enthusiasm.
  Sequence work for impact. Help engineering leads decide what is in scope this sprint, what is next, and what is out.
• Tech Transfer Strategy & Agentification
  Define and drive the New Product Introduction (NPI) launch strategy optimisation - identify the bottlenecks and levers that shorten time from late‑stage development to commercial supply, and quantify the impact on launch timelines.
  Lead the architecture definition for AI‑enabled tech transfer - the target process, data and agent architecture connecting development, sites, Manufacturing Science and Technology (MSAT) and supply chain, in partnership with FDE pods and enterprise architecture.
  Own the definition of the tech transfer package - the content, structure, data and knowledge it must carry - and how it is generated, validated and kept current in an AI native way.
  Drive agentification across the end‑to‑end tech transfer process - decide which steps are automated by agents, which keep humans in the loop, and how agents integrate with site systems (Manufacturing Execution System (MES) and others).
  Lead recipe building for new products - translate process knowledge and the tech transfer package into structured, executable manufacturing recipes (e.g. master and control recipes, ISA-88 style) ready for MES, using agents to draft, check and adapt recipes to each site's equipment and constraints.
• Engineering Partnership & Enablement
  Be the day‑to‑day strategic partner to FDE pods. Convert business ambiguity into crisp problem statements, success metrics, and decision frameworks engineers can act on.
  Run engagement structure end‑to‑end. Scope, success criteria, exit conditions, scaling pathways - drafted, agreed, and revisited.
  Spot reusable patterns that should become platform capability instead of point solutions.
• Stakeholder Alignmen