Staff Full-Stack AI Engineer: Platform, APIs & UX

GSK

Durham (CA)

Hybrid

USD 210,000 - 270,000

Full time

7 days ago

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Job summary

GSK is seeking a Staff Full Stack Engineer for Enterprise AI (EAI) to set technical direction across AI product stacks, integration patterns, and platform standards. The role guides engineers and digital workers through multi-quarter bets, platform decisions, and architecture coherence, while shaping agentic research and AI-enabled experiences.

You will define operating models for agent-enabled work, establish deployment standards, and lead the design of frontend and backend patterns for

Qualifications

Bachelor’s degree or equivalent practical experience in a quantitative field.
10+ years of experience in applied ML or data science and full-stack engineering, including building and operating user-facing AI products.
Experience across AI product stack: modelling pipelines, evaluations, platforms and APIs, modern web frameworks, and data/cloud infra.
Experience designing/operating production AI products: LLM selection, orchestration, tool-use, retrieval and context integration.
Experience defining API, platform and frontend engineering standards for multiple teams, including agent-driven interfaces.
Experience building LLMOps, evaluation and monitoring infrastructure for production AI systems.
Experience scaling agentic research and product workflows (auto-research, 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 and lead end-to-end bets with durable impact.
Define agentic research practice for continuous experimentation and auto-research in production models.
Define operating model for agent-generated engineering work, including gates, approvals and incident handling.
Set the AI-assisted development standard: tooling, workflows, and practices for coding and research agents.
Define API and platform architecture standards adopted across EAI teams (contracts, auth, versioning).
Drive platform coherence across modelling pipelines, evaluations, retrieval, agent orchestration, services, frontends, data and observability.
Shape EAI’s AI integration architecture: LLM selection, orchestration, retrieval, tool-use and MCP protocols.
Own how EAI products draw on the governed context layer with secure retrieval and provenance.
Define frontend engineering standards for AI products and non-deterministic interfaces with trust signals.
Define LLMOps standards for observability, evaluation and governance of production AI systems.