Director, Data & Agentic AI Engineer

Overview

Lead the design of production data systems for a team and its domains within GSK Labs & CMC, defining the architectural, quality and governance standards that the team and its AI digital workers apply.

What you'll do

• Design and build production data pipelines for your specific Labs and CMC domain, serving multiple downstream scientific consumers.
• Implement, enforce and extend the team's patterns for agent-generated work, defining what digital workers build and remediate autonomously versus what requires human review.
• Integrate digital workers into the team's orchestration and observability workflows, applying guardrails, tool designs and human-in-the-loop controls.
• Lead the response to significant data quality incidents within the team's purview, from initial detection through to permanent resolution.
• Set the context standards for the team's Labs & CMC domains, capturing definitions, contracts, classification and lineage in the governed context layer.
• Feed the team's hands-on operational experience upward to evolve the broader Labs and CMC context layer.
• Execute data quality and governance standards for the team's domains, encoding them into the context layer so 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.
• Extend AI-grade data infrastructure for the team's AI system needs, including novel retrieval architectures, improved real-time data flows and smart evaluation datasets.
• Manage the data lifecycle for AI systems within the domain, including versioning, drift monitoring and governance of AI artefacts.
• Design cloud data infrastructure for the team's domains and improve Infrastructure as Code (IaC) and 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 code and documentation quality standards written for a hybrid workforce of humans and digital workers.
• Mentor junior and mid-level engineers through pairing, code review and direct feedback.

What you'll need

• Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
• 8+ years of data engineering experience, including significant time leading the design and operation of complex production data systems.
• Deep expertise with modern warehouses or lakehouses, orchestration and transformation frameworks, and batch and streaming architectures.
• Demonstrated experience designing and operating AI agents or substantial LLM-powered tools inside data engineering workflows, including tool design, evaluation, observability and human-in-the-loop controls.
• Experience leading the design of AI-grade data infrastructure for production AI systems, including evaluation datasets, retrieval architectures and real-time data flows.
• Experience leading data lifecycle management for AI systems, including versioning, drift monitoring and governance of AI artefacts.
• A track record of defining data quality and governance standards for a team or domain that others adopted, with measurable outcomes.
• Experience leading cloud data infrastructure design with infrastructure as code.

Nice to have

• Technical lead for one or more data platforms or significant domains, from first build through to scale.
• Experience with catalogue, metadata, semantic or lineage infrastructure that AI systems consume.
• Experience working within highly regulated industries, such as life sciences, where strict data integrity and auditability are non-negotiable mandates.

Details

• Location: Bengaluru Luxor North Tower.
• Reports to the Director of Data and Agentic AI Engineering (G5).