Description
At GSK, we unite science, technology and talent to get ahead of disease together. We are a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together.
Job Purpose: Support AI engineering delivery on the platform and data fabric workstreams. You configure AI platform environments, document data fabric build outputs, maintain CI/CD pipeline documentation, and develop the automation that keeps the builds moving and surfaces failures before they affect delivery.
About the team : AI ML and Cloud Security is the function through which GSK is transforming security itself. The team designs and builds the agentic framework, a multi-agent platform moving security services across the Chief Security Office from rule-based to AI-driven operation. It owns AI architecture and AI engineering end to end: the agent layer, LLM orchestration, the AI and ML platform, and the data fabric pipeline that CAS designs, builds and tests before a managed service vendor operates it.
Key Responsibilities:
- Configure and maintain AI platform environments for development and test.
- Document data fabric build outputs and keep the build registry accurate.
- Maintain CI/CD pipeline documentation to the standard the vendor needs to operate the fabric once it transfers.
- Develop automation scripts for pipeline health monitoring so failures surface before they affect delivery.
- Build and execute test cases for platform and data fabric builds.
- Maintain build records so the platform estate stays traceable and auditable.
- Work to the architecture specifications provided and raise gaps rather than filling them informally.
- Support senior engineers across the platform and data fabric workstreams.
- Raise defects and blockers early with enough detail to be actionable.
- Build subject matter depth in AI platform engineering and data pipeline delivery.
Basic Qualifications:
- Experienced in AI, platform or software engineering with a relevant technical degree , domain focus: AI Engineering (AI platform and data fabric workstreams)
- Configuring AI platform environments and supporting LLM, agent and RAG pipeline builds , environment setup, embedding and vector index jobs, and evaluation runs
- Building and running test cases for AI components , prompt-injection, output-validation and regression tests for non-deterministic behaviour
- Python automation for AI pipeline health , data quality, ingestion failures, model and API latency, token usage and cost , with automated alerting
- Documenting data fabric builds and CI/CD pipelines to vendor-handover standard and maintaining build records, working to architecture specifications and raising gaps rather than filling them informally.
Preferred Qualifications:
- You have debugged a failing vector index rebuild and can explain what you changed.
- Azure or GCP fundamentals certification (AZ-900, AI-900 or equivalent)
- Working knowledge of prompt-injection test patterns from the OWASP Top 10 for LLM Applications, enough to extend an existing test suite.
- Terraform and Helm for environment provisioning, with enough Git discipline to work in a shared repository.
- Exposure to MLOps tooling , MLflow, model registries or CI/CD for model deployment.
Benefits: The company offers a hybrid working model, with 2/3 days a week onsite at their central London HQ.