Director - Automation Engineer / Product Manager

GSK·India·Onsite

DirectorAIHealthcareEnterprise

Experience

8–10 years

Employment

Full Time

Work mode

Onsite

Level

Director

Location

India

Posted

1 week ago

About the role

GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.

Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.

Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.

Job Purpose

The Engineer, Product Manager leads the design, build and adoption of AI and agentic solutions in the Automation Acceleration Hub, combining hands-on AI engineering with product management to turn high-value opportunities into solutions that change how GSK runs technology. It identifies, shapes and prioritises AI and automation opportunities across DPTF, owns the AI product roadmap and backlog, and stays hands-on in design and delivery where that is what moves the work forward. Reliability and responsible-AI guardrails are built into solutions from the start, with explicit service levels for AI services.

The role is deliberately dual: engineering depth and product ownership sit together rather than being split across two people. AI platform

What you'll do

Identify, shape and prioritise high-value AI and automation opportunities across DPTF, linking each to strategy and to a measurable outcome rather than to a technology.
Lead the design and delivery of AI and agentic solutions that accelerate delivery and operations, staying hands-on where that is what moves the work forward.
Own the AI product roadmap and backlog, steering with OKRs and value metrics under lean portfolio principles.
Build reliability and responsible-AI guardrails into solutions from the start, with clear service levels for AI services rather than best-effort operation.
Partner with the platform domains to embed solutions in their operations, and transition ownership to them as solutions mature.
Champion AI-native ways of working across DPTF, and treat adoption rather than deployment as the point at which a solution has landed.
Measure and report the value and efficiency delivered by AI solutions, including where a solution did not deliver and has been stopped.
Work with AI platform suppliers and model providers, holding them to performance and safety standards rather than accepting vendor assurance.
Maintain a visible pipeline of assessed opportunities, so that Hub capacity is directed by value rather than by whoever asked most recently.
Assess where agentic approaches are appropriate and where they are not, and record the reasoning so the judgement is reusable.
Work within GSK information security, data and quality requirements, including GxP where a solution touches regulated activity, engaging those functions during design rather than at approval.
Hold the boundaries with the Director, AI Platform Architect on AI platform architecture, the Principal AI Process Architect on process design, and the Principal AI Enablement Lead on adoption enablement.

What you'll bring

Degree in a technology, engineering, mathematics or computer science discipline, or equivalent practical experience.
AI, machine learning or cloud certifications desirable, together with product or agile credentials.
Area of Specialisation
AI and agentic solution engineering, covering large language model and agent frameworks, and machine learning operations.
Product management for AI capability, covering opportunity assessment, roadmap and backlog ownership and value measurement.
Experience Required
Overall 18 years and typically 8-10 years in engineering or technology delivery, including at least three years building AI, machine learning or automation solutions in production rather than in pilots.
Strong hands-on experience with AI and agentic solutions, including large language model and agent frameworks.
Product management experience, covering opportunity assessment, roadmap and backlog ownership and value measurement.
Track record taking an opportunity through to delivered and adopted value, not to proof of concept.
Practical responsible-AI awareness, including where a use case should not proceed.
Experience with machine learning operations, or an automation or robotic process automation background.