Define the architecture standards and shared frameworks that digital workers use to generate, test, deploy and monitor pipelines, reducing the integration cost of each new AI initiative.
Set the operating model for agent-generated engineering work: what digital workers do autonomously, what requires human approval, how quality is evaluated and how incidents escalate. Lead the incidents digital workers cannot resolve, from detection to permanent resolution.
Required skills
10+ years in data engineering, including substantial time defining data architecture for production AI or ML systems at significant scale or complexity.
Deep, hands-on experience building AI-grade data infrastructure: feature stores, vector databases, retrieval architectures, streaming data flows and evaluation datasets.
Experience building context, metadata or semantic infrastructure that AI systems consume, such as catalogues, business glossaries, knowledge graphs or ontologies, exposed to agents over protocols such as MCP.
Experience designing and operating AI agents or LLM-powered systems inside data workflows, including evaluation, guardrails and human-in-the-loop controls.
A track record of defining data quality, lineage and governance standards for AI pipelines, including privacy, consent, PII handling and auditability, adopted across multiple data domains.
Expert command of cloud data platforms and infrastructure as code, and a record of setting engineering standards that others adopted
Education
Bachelor's degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.
Preferred skills
Prior Staff, Principal or equivalent experience in an organisation operating a non-trivial data platform for AI.
Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.
Public technical contributions, such as talks, writing or open source, on data architecture for AI, context engineering or agent-operated data engineering.
#AGTech
Skills
Artificial Intelligence (AI), Data Analysis, Data Architecture Development, Database Engineering, Databricks Unified Data Analytics Platform, Data Engineering, Data Transformation, ETL Development, Machine Learning (ML), Python (Programming Language), Structured Query Language (SQL)
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
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