Senior, Full Stack Engineer

GSK•Philadelphia, PA

About The Position

Enterprise AI (EAI) builds AI products that combine modelling work with user-facing applications and brings them into production. Full Stack Engineers work across data, models, platforms, APIs and interfaces. In EAI’s operating model, digital workers generate services, APIs, interface components and tests, run evaluations, and monitor production systems; engineers set the architecture and standards and own the quality of what ships. EAI products and agents use a governed context layer to retrieve data, meaning, permissions and history when needed. 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 owns architecture, quality and outcomes; establishes team standards for APIs, AI integration, interfaces and observability; designs agentic research systems that support continuous experimentation and iteration; and mentors junior and mid-level engineers. The role is based in the United States, with flexible working options, including hybrid arrangements depending on team needs.

Requirements

Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
At least 8 years of combined experience across applied machine learning or data science and full-stack engineering, including significant experience 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; examples include React or Next.js, Python or TypeScript, and AWS, Azure or GCP.
Experience leading the design of API layers serving multiple consumers, with integration contracts and non-functional requirements for other teams or consumers.
Experience designing production-scale AI orchestration, including 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 for continuous research or product iteration, and AI-assisted development patterns adopted by a team.

Nice To Haves

Master's or PhD in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
Experience as technical lead for one or more AI products combining modelling and full-stack delivery, from initial build through scale.
Experience building or improving 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.

Responsibilities

Set technical direction for the product area and its modelling pipelines, including system boundaries, evaluation strategies, and trade-offs among performance, reliability and cost.
Design agentic research systems, including agents that run experiments and synthesise findings, monitor and improve production models, and scaffold and iterate on features.
Partner with product, design and research to shape product work, challenge weak ideas and communicate trade-offs.
Define patterns for agent-generated work, including what digital workers can build or change independently, required evaluation gates, and when human review is needed.
Establish safe and effective AI-assisted development patterns for coding and research agents.
Lead the design of API layers serving multiple consumers, define integration contracts and non-functional requirements, and improve API standards across the product area.
Guide backend architecture decisions and lead shared infrastructure and tooling that reduces integration costs across teams, driving adoption.
Design production-scale AI orchestration architectures covering agent frameworks, multi-step workflows, retrieval, tool use, guardrails and integration patterns.
Define standards for AI integration, evaluation and responsible deployment, including how products and agents use the governed context layer with clear permissions and provenance.
Resolve production AI failure modes.
Lead frontend architecture, define UI patterns and the team's component library, and guide UX engineering quality.
Drive AI interface design that achieves measurable adoption and includes confidence signals, error states, provenance and human-override paths.
Lead LLMOps and observability strategy, including evaluation pipelines and production monitoring at scale.
Define standards for AI model lifecycle management, versioning and inference-cost tracking across the product area.
Set engineering standards for humans and digital workers, with measurable improvements in test coverage, API contract quality and deployment reliability.
Mentor junior and mid-level engineers through pairing, reviews and direct feedback, improving their delivery capability.
In the first 12 months, lead delivery of a significant AI product area; define agent-generated-work and API, AI integration and interface standards; ship an agentic research system that measurably accelerates experimentation or product iteration; and mentor engineers toward greater capability and independence.