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 Senior Full Stack Engineer leads the design and delivery of a significant AI product area, spanning modelling workflows and user-facing applications. The role sets the team's standards for APIs, AI integration, interfaces and observability, designs the agentic research systems that drive continuous iteration, and mentors junior and mid-level engineers.
- Leads a significant AI product area across modelling workflows, platforms and APIs and user-facing interfaces, owning architecture, quality and outcomes.
- Defines how the team builds with digital workers through the API, integration and interface standards and the review patterns their work must meet within EAI's operating model.
- Designs agentic research systems that drive continuous experimentation, evaluation and product iteration, and raises the engineering bar by mentoring junior and mid-level engineers.
Key Responsibilities
Technical Leadership & Research Practice
- Set technical direction for the product area and its modelling pipelines, defining boundaries, evaluation strategies and defensible trade-offs on performance, reliability and cost.
- Design agentic research systems: auto-research agents that run experiments and synthesise findings, agents that monitor and improve production models, and agents that scaffold and iterate on features.
- Partner with product, design and research to shape what gets built, challenging weak ideas and communicating trade-offs clearly.
Agent-Operated Engineering
- Define the team's patterns for agent-generated work within EAI's operating model: what digital workers build and change on their own, which evaluation gates their work must pass, and when it needs human review.
- Set the team's AI-assisted development patterns for coding and research agents, establishing safe, effective use the team adopts.
Platforms & APIs
- Lead the design of API layers that serve multiple consumers, defining integration contracts and non-functional requirements for the team.
- Set or improve API design standards used across the product area, and guide others on backend architecture decisions.
- Lead the creation of shared infrastructure and tooling that materially reduces integration cost across multiple teams, and drive its adoption.
AI Integration & Context
- Design AI orchestration architectures at production scale: agent frameworks, multi-step workflows, retrieval, tool use, guardrails and integration patterns.
- Define the team's standards for AI integration, evaluation and responsible deployment, including how the product area's products and agents draw on the governed context layer with clear permissions and provenance. Resolve production AI failure modes as they arise.
AI-Native Experience
- Lead frontend architecture for the product area, defining the UI patterns and component library the team works from.
- Drive AI interface design that achieves measurable adoption, with confidence signals, error states, provenance and human-override paths, and guide others on UX engineering quality.
LLMOps & System Health
- Lead the team's LLMOps and observability strategy, designing evaluation pipelines and production monitoring at scale.
- Define standards for AI model lifecycle management, versioning and inference cost tracking across the product area.
Standards & Mentorship
- Set engineering standards for the team, written to be followed by humans and digital workers alike, with measurable improvements in test coverage, API contract quality and deployment reliability.
- Mentor junior and mid-level engineers through pairing, review and direct feedback, with visible improvement in their delivery capability.
Why You? Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals:
- Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience
- 8+ years of combined experience across applied ML or data science and full-stack engineering, with significant time leading delivery of complex, user-facing AI products.
- Expertise across the AI product stack, including modelling and evaluation, modern frontend frameworks, backend services and cloud deployment, such as React or Next.js, Python or TypeScript, and AWS, Azure or GCP.
- Experience leading the design of API layers that serve multiple consumers, with integration contracts and non-functional requirements others build to.
- Experience designing AI orchestration at production scale: LLM integration, retrieval, tool and function calling, agent frameworks, evaluation, and cost, latency and quality trade-offs.
- Experience leading frontend architecture and LLMOps for a product area, including evaluation pipelines, production monitoring and model lifecycle management.
- Experience designing and operating agentic systems that drive continuous research or product iteration, and AI-assisted development patterns a team adopted.
Preferred Qualification
If you have the following characteristics, it would be a plus:
- Master's or PhD preferred, in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
- Technical lead for one or more AI products combining modelling and full-stack delivery, from first build through to scale.
- Built or improved internal developer or research tooling, particularly for AI-assisted development and auto-research.
- Experience in a regulated industry, such as life sciences, where data integrity and auditability are mandatory.
- Leads the design of API layers serving multiple consumers; defines integration contracts and non-functional requirements for the team and Sets or improves API design standards used across a product area; guides others on backend architecture decisions
- Designs AI orchestration architectures at production scale, including agent frameworks, multi-step workflows and how products draw on the governed context layer and defines team standards for AI integration, evaluation and responsible deployment; resolves production AI failure modes
- Leads frontend architecture for a product area; defines the UI patterns and component library the team works from.
- Drives AI interface design that achieves measurable adoption; guides others on UX engineering quality
- Leads the team's LLMOps and observability strategy; designs evaluation pipelines and production monitoring at scale and defines standards for AI model lifecycle management, versioning and inference cost tracking across a product area
- Sets engineering standards for the team, including patterns for agent-generated work; drives measurable improvements in test coverage, API contract quality or deployment reliability.
- Designs agentic research systems in production: auto-research, evaluation-driven improvement loops and agent-driven prototyping.
What Success Looks Like
In the first 12 months, the person in this role will have:
- Led delivery of a significant AI product area spanning modelling and application work, owning architecture and outcomes.
- Defined the team's patterns for agent-generated work and its standards for APIs, AI integration and interfaces, with measurable improvements in quality or reliability.
- Designed and shipped an agentic research system that measurably accelerated experimentation or product iteration.
- Mentored junior and mid-level engineers who now deliver with greater capability and independence.
Location and Working Model
- This role is based in the United States.
- Flexible working options available, including hybrid arrangements depending on team needs.
#AGTech
Skills AI Frameworks, Algorithm Optimization, Artificial Neural Networks (ANNS), Computer Programming, Deep Learning, Feature Engineering, Google Cloud Platform (GCP) for Machine Learning, Machine Learning Algorithms, Machine Learning Operations, Microsoft Azure Data and AI, ML Frameworks, Model Evaluation, Model Training, Model Validation, Performance Optimizations, Software Development Life Cycle (SDLC)
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
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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