Staff Full-Stack Engineer - AI Product Systems Lead

GlaxoSmithKline

United States

Hybrid

USD 190,000 - 270,000

Full time

6 days ago

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Job summary

GlaxoSmithKline is seeking a Staff Full Stack Engineer for Enterprise AI (EAI) to set technical direction across AI products, modelling pipelines, platforms, and APIs. You will own integration/orchestration architecture and define standards for agent-driven development, ensuring scalable, reliable delivery across teams.

The role emphasizes AI-native UX, LLM integration, and end-to-end engineering leadership.

Qualifications

Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related quantitative field, or equivalent practical experience.
10+ years of experience in applied ML or data science and full-stack engineering, including experience building and operating user-facing AI products.
Experience across the AI product stack, including modelling pipelines, evaluations, platforms and APIs, modern web frameworks, and data and cloud infrastructure.
Experience designing and operating production AI products, including LLM selection and integration, orchestration and tool-use architectures, retrieval and context integration.
Experience defining API, platform and frontend engineering standards for multiple teams, including interfaces for non-deterministic, agent-driven behaviour.
Experience building LLM operations, evaluation and monitoring infrastructure for production AI systems.
Experience defining and scaling agentic research and product workflows, such as auto-research and continuous evaluation.
Experience setting multi-quarter technical strategy across multiple engineering teams.

Responsibilities

Set multi-quarter technical direction for the AI product stack across EAI, including modelling workflows, platforms and APIs.
Own the integration and orchestration architecture for EAI products and agents.
Define EAI's agentic research practice for continuous experimentation and auto-research.
Set the operating model for agent-generated engineering work and determine evaluation gates and human approvals.
Define API and platform architecture standards adopted across EAI engineering teams.