Senior Full Stack Engineer - Enterprise AI

About the role

GSK's Enterprise AI (EAI) team builds AI products that combine real modelling work with user-facing applications and ships them to production. Full Stack Engineers move across data, models, platforms, APIs and interfaces in a single workflow, setting the architecture and standards and owning the quality of what ships. This role is for a Full Stack Engineer II who independently builds and ships AI-integrated features, working closely with GSK business teams to prototype against real data and workflows at a days-to-weeks cadence.

Key responsibilities

• Own use cases from problem framing through working prototype and first production version, at a days-to-weeks cadence and without close supervision.
• Qualify incoming requests: challenge underspecified problems, assess feasibility and define scope before committing to work.
• At the end of each piece of work, form and defend an evidence-based recommendation to continue or stop, presented clearly to business and EAI leadership.
• Work closely with business teams, prototyping against real data and workflows and building trusted relationships with product owners and engineering leads.

Required profile

• 16+ years of overall professional experience and 10+ years of full-stack or AI/ML engineering experience, including production deployments.
• Demonstrated ability to turn ideas into working software quickly, independently and under time pressure; a startup, lab or innovation-team background is an advantage.
• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related field, or equivalent practical experience.
• Preferred: prior experience as a Full Stack Engineer, Customer Engineer or Solutions Architect, ideally at an AI-native company or hyperscaler; experience in regulated industries such as life sciences, healthcare or financial services, and familiarity with GxP, GDPR and SOX.

Required skills

• Strong Python and at least one of TypeScript, JavaScript, Go or similar, with backend services and complete UI features.
• Hands-on GenAI patterns: agents, multi-agent orchestration, RAG, tool use, prompt engineering and evaluation, including evaluation pipelines and LLM monitoring in production.
• Daily use of AI coding agents as the primary mode of development.
• Raising team standards through code review and documentation, and contributing reusable engineering patterns.
• Preferred: fine-tuning fundamentals and Model Context Protocol (MCP) integrations.