Senior Principal - Automation / Prompt / Agent Engineer

GSK; Bengaluru; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

GSK in Bengaluru, India seeks an Automation Enablement Lead to scale AI delivery across DPTF. You own the golden pattern library, drive AI-First practices, and coach platform teams to build capable AI solutions outside central hubs.

The role partners with Director-level stakeholders to encode architectural standards, governance and pipelines into reproducible patterns, enabling rapid, governed AI delivery across the organization.

Qualifications

• Degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• Professional certifications desirable (e.g., cloud or machine learning platform certifications, coaching or technical enablement accreditation).

Responsibilities

• Own the golden pattern library and paved paths for AI delivery across DPTF, publishing the architectural standards set by the Director, Automation Platform Architect in a form platform teams can adopt, so that many teams build in one dialect instead of each inventing its own.
• Drive adoption of AI-First delivery practice across platform teams within DPTF's governed delivery lifecycle, so that eval gates, cost governance and provenance controls are inherited by any team using the paved paths.
• Drive AI skills uplift across platform engineering, building the capability that removes the need for dedicated AI headcount in every team.
• Run the AI community of practice and the associated governance forums, spreading what works between platform teams and surfacing what is not working.
• Publish and maintain the pattern library, reference implementations and guidance so that teams can adopt without asking.
• Work with talent and hiring leads so that AI fluency becomes an expectation of new platform engineering hires, not a specialism.
• Coach platform teams through their first AI deliveries, withdrawing support deliberately as their capability matures.
• Feed practical experience from platform teams back into the patterns, so standards reflect what works in delivery, not what looks right centrally.
• Translate Enterprise AI standards and Responsible AI policy into patterns and pipeline controls that platform teams can actually apply.
• Measure adoption of patterns, pipeline usage and AI capability across platform teams, and target enablement where uptake is weakest.
• Identify where a capability should become a shared primitive rather than a repeated pattern, and raise it with the Director, Automation Engineering / Product Manager.
• Hold the boundaries with the Director, Automation Engineering / Product Manager on solution delivery and shared primitives, the Director, Automation Platform Architect on architectural standards and governance, the Senior Principal, Agile CoE on DPTF delivery method, AI Ops on production readiness, and the platform teams on their own delivery.

Skills

Architectural Review

Asset Life Cycle

Data Integrity

Design Assurance

Digital Fluency

Digital Workplace

GxP Regulations

GxPs

Lean Management

Manufacturing Technologies

Pharmaceutical Manufacturing

Problem Solving

Risk Awareness

Standards Compliance

Technology Architecture

Education

Degree in Computer Science, Engineering, or a related discipline

Job description

Business Introduction

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.

The Automation Enablement Lead makes it possible for DTPF platform teams to build AI capability themselves. The role owns the golden pattern library and paved paths teams build against, drives adoption of AI-First delivery practice within DPTF's governed delivery lifecycle, runs the community of practice, and drives the AI skills uplift that turns platform engineers into people who can deliver AI without central support.

Platform teams own the outcomes. This role makes those outcomes achievable, and its success is measured by how much AI work happens outside the hub, not inside it. Solution delivery sits with the Director, Automation Engineering / Product Manager. Architectural standards and governance sit with the Director, Automation Platform Architect, and this role packages them into something teams can adopt. Delivery method for DPTF sits with the Senior Principal, Agile CoE, and the governed delivery lifecycle itself is owned through the GSK quality management system.

Key Responsibilities

• Own the golden pattern library and paved paths for AI delivery across DPTF, publishing the architectural standards set by the Director, Automation Platform Architect in a form platform teams can adopt, so that many teams build in one dialect instead of each inventing its own.
• Drive adoption of AI-First delivery practice across platform teams within DPTF's governed delivery lifecycle, so that eval gates, cost governance and provenance controls are inherited by any team using the paved paths.
• Drive AI skills uplift across platform engineering, building the capability that removes the need for dedicated AI headcount in every team.
• Run the AI community of practice and the associated governance forums, spreading what works between platform teams and surfacing what is not working.
• Publish and maintain the pattern library, reference implementations and guidance so that teams can adopt without asking.
• Work with talent and hiring leads so that AI fluency becomes an expectation of new platform engineering hires, not a specialism.
• Coach platform teams through their first AI deliveries, withdrawing support deliberately as their capability matures.
• Feed practical experience from platform teams back into the patterns, so standards reflect what works in delivery, not what looks right centrally.
• Translate Enterprise AI standards and Responsible AI policy into patterns and pipeline controls that platform teams can actually apply.
• Measure adoption of patterns, pipeline usage and AI capability across platform teams, and target enablement where uptake is weakest.
• Identify where a capability should become a shared primitive rather than a repeated pattern, and raise it with the Director, Automation Engineering / Product Manager.
• Hold the boundaries with the Director, Automation Engineering / Product Manager on solution delivery and shared primitives, the Director, Automation Platform Architect on architectural standards and governance, the Senior Principal, Agile CoE on DPTF delivery method, AI Ops on production readiness, and the platform teams on their own delivery.

Professional Qualifications

• Degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• Professional certifications desirable (e.g., cloud or machine learning platform certifications, coaching or technical enablement accreditation).

Area of Specialisation

• AI enablement and capability building, covering pattern libraries, reference implementations and technical community practice.
• AI-First delivery practice within a governed lifecycle, including paved paths, eval gates and inherited controls.

Why is this level of Education Required?

• A strong engineering foundation is required because patterns and pipelines are only adopted when the person setting them is credible with the engineers expected to use them.

Related Experience Required

• Overall 15 years and 8-10 years in engineering, including hands-on software development, AI or, machine learning delivery experience.
• Experience setting patterns, standards or reference implementations adopted by teams outside direct control.
• Experience embedding automated quality and evaluation gates into a delivery toolchain that operates within a governed lifecycle.
• Experience building capability in other engineers through coaching, community practice or structured enablement.
• Experience translating enterprise standards and policy into something delivery teams can practically apply.
• Experience measuring adoption and acting on where uptake is weak.
• Experience working across multiple technology domains rather than within a single platform.
• Familiarity with Responsible AI requirements and how they are implemented in a delivery pipeline.

Desirable

• Hands-on experience building AI-first automation (agents, workflows, integrations) rather than just scoping it.
• Comfortable technical fluency in modern AI/ML - enough to design and build realistic automations, not just specify them.
• Working knowledge of at least one pharma value chain area (R&D, manufacturing, or commercial), including how work actually gets done on the ground.
• Background in process documentation (RACI/SOP) or workflow redesign, translating manual processes into automated ones.

Why is this Level of Experience Required?

• The role changes how a large engineering population works without any authority over it, which requires enough hands-on credibility to be listened to and enough experience to know which standards are worth insisting on.

Skills/Background

• Deep practical AI engineering knowledge, current enough to be credible with specialist engineers.
• Ability to build standards that teams adopt willingly, because they are useful, not because they are mandated.
• Strong coaching and facilitation skills across teams in different locations and domains.
• Excellent written communication, since patterns and guidance are consumed without the author present.
• Judgement on when to insist on a standard and when to let a team diverge.
• Willingness to withdraw support so that teams become self-sufficient.

#AGTech

Skills

Architectural Review, Asset Life Cycle Management, Data Integrity, Design Assurance, Digital Fluency, Digital Workplace, GxP Regulations, GxPs, Lean Management, Manufacturing Technologies, Pharmaceutical Manufacturing, Problem Solving, Risk Awareness, Standards Compliance, Technology Architecture

Why GSK?

Uniting science, technology and talent to get ahead of disease together.

Inclusion at GSK:

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at [e-mail] to discuss your needs.