Associate Director, Data & Agentic AI Engineering

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 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.

The Associate Director, Data & Agentic AI Engineering, leads the design of production data systems for a team and its domains within Labs & CMC.

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 the team's patterns for agent-generated work, defining 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 the 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 the 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

• 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.