Principal Engineer, Data & Agentic AI Engineering

Overview

Principal Engineer, Data & Agentic AI Engineering within GSK's Labs CMC Data, Agentic and Knowledge Engineering (DAKE) group. Independently owns a specific Labs & CMC data domain, designing pipelines for complex scientific use cases and directing digital workers that build and operate them.

What you'll do

• 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 the 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.
• Own the context for your Labs & CMC domain in the governed context layer: definitions, contracts, lineage and governance documentation.
• Contribute reusable components to the broader Labs and CMC context layer, such as metadata enrichment, classification or lineage capture.
• Build an end-to-end data quality framework for your domain, encoding standards 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.
• Design and build feature stores, vector databases or retrieval infrastructure for production AI systems within Labs & CMC, with data contracts for each AI consumer.
• Manage training datasets, evaluation sets and data versioning for AI projects in production, and contribute to evaluation dataset design.
• Build cloud data infrastructure independently using infrastructure as code, and contribute reusable components to the shared data platform.
• Design observability into pipelines from the start, and resolve infrastructure issues with minimal disruption to scientific consumers.
• Own incident response for your domain, with clear post-incident analysis and follow-through.
• Raise team technical quality through code review and documentation, working with senior engineers and domain scientists to translate ambiguous requirements into well-scoped designs.

What you'll need

• 6+ years of experience building and operating production data pipelines and data models.
• Deep SQL and Python, with strong hands-on expertise in modern warehouses or lakehouses and in orchestration and transformation frameworks, such as Databricks, BigQuery, Airflow, dbt or Spark.
• Experience designing and operating at least one AI agent or LLM-powered tool inside a data workflow, and fluent, daily use of AI coding assistants and agents with sound judgement on what to accept, change or reject.
• Experience designing and building feature stores, vector databases or retrieval infrastructure for production AI systems, including data contracts for at least one AI consumer.
• Experience managing training datasets, evaluation sets and data versioning for at least one AI project in production.
• A track record of building data quality frameworks and lineage for a domain, and of applying privacy, consent and compliance controls in AI data pipelines.
• Experience building cloud data infrastructure independently with infrastructure as code, with observability designed in from the start.
• Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.

Nice to have

• Led the design and delivery of a component of a larger data platform.
• Experience with agentic systems in production: multi-step tool use, evaluations, guardrails and observability applied to operational data problems.
• Experience with quality frameworks and with catalogue, metadata or lineage tooling.
• Experience in regulated or high-stakes data environments, such as life sciences, labs, CMC, or healthcare, where strict data integrity is a mandate.

Details

• Location: Bengaluru Luxor North Tower.