Principal - AI ML and Cloud Security

GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.

Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.

Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.

The deepest hands-on engineering role in AI Engineering. You personally build the most complex AI solutions Cyber Architecture and Solutions delivers, lead the hardest engineering problems in the agentic framework, and develop the reusable components the rest of the team builds on.

This role will provide you the opportunity to lead key activities to progress your career. These responsibilities include some of the following:

• →Personally build the most complex AI solutions Cyber Architecture and Solutions delivers, using LangChain, LlamaIndex, vector databases and production Python.

• →Build the shared SDK, evaluation harness and reusable components the agent and platform workstreams both depend on.

• →Lead the hardest engineering problems in the agentic framework build.

• →Develop reusable engineering components, libraries and patterns the wider team builds on.

• →Build to the architecture specification and raise design problems early rather than engineering around them.

• →Write tests and documentation to vendor handover standard.

• →Contribute to CI/CD pipelines and engineering tooling for AI workloads.

• →Integrate agents with Splunk, Microsoft Sentinel, CrowdStrike and ServiceNow — writing and maintaining the connectors, not specifying them.

• →Mentor engineers and review their code properly.

• →Bring one toolchain change a quarter to the team with a working spike and a migration cost, not a recommendation.

• →Use approved AI tools in your day-to-day work to improve your own productivity and your team's ways of working, sharing what works and applying sound judgement, including validating outputs and following GSK's Responsible AI, privacy and security requirements.

• →Design components and APIs within the solutions you build, and document the trade-offs behind each design decision so the rationale survives handover.

• →Apply GxP and data integrity expectations where what you build touches validated systems.

• 10+ years in AI or software engineering with a relevant technical degree — domain focus: AI Engineering (agentic framework build and AI solution engineering); hands-on engineering of complex AI solutions in production, building reusable components, libraries and patterns for agent and platform workstreams

• Agent build in production Python and Kubernetes — LLM integration, prompt-to-action engineering, LangChain or LlamaIndex orchestration, tool and function calling, and evaluation of non-deterministic output

• RAG pipeline engineering end to end — chunking and embedding strategy, vector database and index design, retrieval tuning and evaluation, hallucination and grounding checks

• LLM application security in code — direct and indirect prompt injection defences, output validation, secrets handling, tool-call allow-listing and audit logging

• Delivering to architecture specification with tests and documentation to vendor-handover standard — raising design problems back to architecture rather than engineering around them, and mentoring analysts

• CI/CD pipelines for AI and ML platforms

• LLM evaluation frameworks (Ragas, DeepEval or equivalent), fine-tuning and prompt optimisation

• AI red-teaming tools such as Garak or PyRIT

• Cloud platform engineering on Azure or GCP

• Code review of junior engineers' work
• Typical tools and technologies: Production Python and Kubernetes, LangChain, LangGraph and LlamaIndex orchestration, vector databases and retrieval tuning across pgvector, Pinecone and Weaviate, model gateways such as LiteLLM, evaluation harnesses for non-deterministic output, reusable library and SDK design, CI/CD and automated testing.
  

#AGTech

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.

As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.

Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.

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