Description
Job Purpose:
Enterprise AI (EAI) 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. In our operating model, digital workers do much of the hands-on build. They generate services, APIs, interface components and tests, run evaluations and monitor what is in production. Our engineers set the architecture and standards that work is built to, and own the quality of what ships.
EAI products and agents draw on a governed context layer that makes data, meaning, permissions and history retrievable at the moment they are needed. Full Stack Engineers build the products and agents that consume it, and decide how that context reaches users.
The Full Stack Engineer II 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. The role owns the quality of what digital workers deliver in those features, makes evidence-based calls on what to continue or stop, and contributes reusable components others adopt.
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 asks: 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 skills:
- 6+ years of full-stack or AI/ML engineering experience, including production deployments.
- Demonstrated ability to take ideas from sketch to working software at speed, independently and under time pressure. A startup, lab or innovation-team background is an advantage.
- Strong proficiency in Python and at least one of TypeScript, JavaScript, Go or similar, with experience designing and shipping backend services and complete UI features.
- Hands-on experience with GenAI patterns: agents, multi-agent orchestration, RAG, tool use, prompt engineering and evaluation, including evaluation pipelines and LLM-specific monitoring in production.
- Daily use of AI coding agents as the primary mode of development, with sound judgement on what to accept, change or reject.
- A track record of raising team standards through code review and documentation, and of contributing reusable engineering patterns that others adopted.
Education :
- Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related field, or equivalent practical experience.
Benefits:
None mentioned.