# Principal Engineer, Data & Agentic AI Engineering at GSK

GSK is hiring a Principal Engineer, Data & Agentic AI Engineering for a remote role open to applicants in India. Level rates it Works on AI ●●●○; you can [apply on GSK](https://jobsbylevel.com/go/4c6e38b9-cc0c-44c0-b5a6-f4edbd07f49f?utm_medium=ai-agent).

AI Level 3, AI centrality 66 out of 100. Remote (Bengaluru Luxor North Tower).

## Details

- Company: [GSK](https://jobsbylevel.com/companies/gsk)
- AI level: AI Level 3 (score 66 out of 100)
- Location: Remote (Bengaluru Luxor North Tower)
- Posted: November 11, 2026
- Apply: https://jobsbylevel.com/go/4c6e38b9-cc0c-44c0-b5a6-f4edbd07f49f?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=principal-engineer-data-agentic-ai-engineering-at-gsk-36d438

## Prepare for this job

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## Description

About the Role 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 Senior Data and Agentic Engineer, the Principal Engineer, Data & Agentic AI Engineering, independently owns a specific Labs & CMC data domain. The role designs pipelines for complex scientific use cases, owns the quality of what digital workers deliver in that domain, and keeps its context accurate and audit-ready. It also contributes reusable components to the platform and works collaboratively to raise technical quality across the team. Why This Role Exists Owns a Labs & CMC data domain end-to-end, with clear accountability for the pipelines, models and datasets digital workers build and operate in it, and sound independent judgement on design choices. Makes digital workers more effective in that domain by improving the frameworks, tools and evaluations they use, so manual intervention falls measurably over time. Delivers quality-assured scientific data to AI systems through data contracts, governed context and quality frameworks that hold up to the strict auditability mandates of life sciences. Key Responsibilities Domain Level Data and Agentic Engineering Design pipelines for complex Labs & CMC use cases and direct digital workers to build and operate them, with clear, defensible architectural trade-offs. Own the quality of agent-generated work in your scientific domain: review, test and approve it within the remit our operating model sets, and escalate what falls outside it to senior engineering leadership. Improve how digital workers operate in your domain by refining the frameworks, tools and evaluations they use, iterating on reliability signals so manual intervention falls measurably over time. Context & Semantics Own the context for your Labs & CMC domain in the governed context layer: definitions, contracts, lineage and governance documentation that humans and digital workers rely on. Contribute reusable components to the broader Labs and CMC context layer, such as metadata enrichment, classification or lineage capture, that other scientific domains can adopt. Data Quality & Governance Build an end-to-end data quality framework for your domain, encoding standards directly into the context layer with lineage tracking and governance documentation that passes audit. Apply privacy, IP, and consent controls in AI data pipelines, and manage sensitive R&D data appropriately across the data lifecycle. AI Data Infrastructure & Lifecycle Design and build feature stores, vector databases or retrieval infrastructure for production AI systems within Labs & CMC, with data contracts that give each AI consumer consistent, quality-assured inputs. Manage training datasets, evaluation sets and data versioning for AI projects in production and contribute to evaluation dataset design with a clear view of how data decisions affect scientific model quality. Platform & Infrastructure Build cloud data infrastructure independently using infrastructure as code and contribute reusable components to the team's shared data platform to reduce setup time for new Labs and CMC

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/principal-engineer-data-agentic-ai-engineering-at-gsk-36d438

Source: https://jobsbylevel.com/jobs/principal-engineer-data-agentic-ai-engineering-at-gsk-36d438

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Level. https://jobsbylevel.com/jobs/principal-engineer-data-agentic-ai-engineering-at-gsk-36d438.

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