# Director, Data & Agentic AI Engineer at GSK

GSK is hiring a Director, Data & Agentic AI Engineer for a remote role open to applicants in India. Level rates it Little AI ●○○○; you can [apply on GSK](https://jobsbylevel.com/go/1b87b08c-342a-4bd5-8409-9255e90c2577?utm_medium=ai-agent).

AI Level 1, AI centrality 16 out of 100. Remote (Bengaluru Luxor North Tower).

## Details

- Company: [GSK](https://jobsbylevel.com/companies/gsk)
- AI level: AI Level 1 (score 16 out of 100)
- Location: Remote (Bengaluru Luxor North Tower)
- Posted: November 11, 2026
- Apply: https://jobsbylevel.com/go/1b87b08c-342a-4bd5-8409-9255e90c2577?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=director-data-agentic-ai-engineer-at-gsk-91b22b

## Prepare for this job

The job page lists 2 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/director-data-agentic-ai-engineer-at-gsk-91b22b

## Description

Job Purpose: GSK R&D and Digital & Tech's collective goal is to deliver GSK impact, including the acceleration of the discovery and development of medicines and vaccines to patients. The Labs and CMC Tech function is central to this mission, driving the reimagining and business transformation of our global laboratory and CMC processes. We are actively transitioning from managing raw scientific data to generating structural scientific knowledge—the foundation for how R&D will discover future medicines in a technology-enabled ecosystem. The Labs CMC Data, Agentic and Knowledge Engineering (DAKE) group architects the data, agentic and knowledge foundations upon which critical R&D processes and scientific workflows depend. Crucially, our operating model relies on a hybrid workforce: AI agents function as "digital workers" to execute hands-on engineering tasks. They generate pipelines, test and deploy them, monitor them in production and remediate failures. Reporting into the Director of Data and Agentic AI Engineering (G5), the Associate Director, Data & Agentic AI Engineering, leads the design of production data systems for a team and its domains within Labs & CMC. The role defines the architectural, quality, and governance standards the team and its digital workers apply, leads the team's AI-grade data infrastructure, and mentors junior and mid-level engineers. Key Responsibilities Domain-Level Data and Agentic Engineering Design and build production data pipelines for your specific Labs and CMC domain, serving multiple downstream scientific consumers. Implement, enforce and extend our team's patterns for agent-generated work, defining exactly what digital workers build and remediate autonomously versus what requires human review within your domain. Integrate digital workers into your team's specific orchestration and observability workflows, applying clear guardrails, tool designs, and human-in-the-loop controls. Lead the response to significant data quality incidents within your team's purview, from initial detection through to permanent resolution. Context & Semantics Layer Set the context standards for the team's Labs & CMC domains: how definitions, contracts, classification and lineage are captured in the governed context layer, so humans and digital workers can rely on them. Actively contribute to the broader Labs and CMC context layer by feeding your team's hands-on operational experience upward to evolve the platform. Data Quality & Governance Execute data quality and governance standards for your team's domains, encoding them directly into the context layer so both humans and digital workers apply them by default. Lead local data quality initiatives to achieve measurable outcomes, such as reduced error rates, improved audit readiness, and better downstream AI performance. AI Data Infrastructure & Lifecycle Extend our AI-grade data infrastructure for your team's specific AI system needs, including novel retrieval architectures, improved real-time data flows, and smart evaluation datasets. Manage the data lifecycle for AI systems within your domain, including versioning, drift monitoring, and the governance of AI artefacts. Platform Engineering & Mentorship Design cloud data infrastructure for your team's domains, improve our Infrastructure as Code (IaC) and its deployment reliability. Build shared platform components used by multiple engineers and digital workers to reduce setup time for new Labs and CMC data projects. Set and enforce the code and documentation quality standards for your team, written explicitly for a hybrid workforce of humans and digital workers Mentor junior and mid-level engineers through pairing, code review, and direct feedback to visibly improve their capability and independence. Required Qualifications and Experience Education Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience. 8+ years of data engineering experience, including

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