Senior Full Stack AI Engineer

About the Role: GSK’s Enterprise AI team builds AI solutions that prove value fast and scale to production. The Forward Deployed Engineer is the engineer GSK sends in to find out whether an AI idea will work — embedded with business unit teams, proving ideas through PoCs, PoVs, and MVPs at a days-to-weeks cadence, then handing the proven solution off to the Sr. Solutions Engineer for scale-up. The pattern is proven by AI-native companies — Anthropic, Palantir, OpenAI — and this role brings that model to GSK. This senior engineer role embeds directly with business unit teams — product owners, engineering leads, and the wider BU — to understand the problem and execute. Most prototyping work is AI-augmented: AI coding assistants and multi-agent workflows handle the bulk of routine coding, freeing the FDE to focus on framing, judgement, and validation. The role works directly inside the customer’s business and emerges with working software and a defensible value hypothesis — not slideware. The FDE does not carry use cases through to long-term operations — clean handoff to the Sr. Solutions Engineer is part of the role. Why This Role Exists: Enterprise AI’s impact depends on engineers who can close the gap between what AI can do and what the business needs — fast, embedded, and without requiring the business to specify everything upfront. The FDE is that engineer. • Proves AI ideas fast — shipping working prototypes at days-to-weeks cadence before significant investment is committed. • Works directly inside the business — not at arm’s length — building the trust and domain understanding that separate useful AI from academic AI. • Builds the organisation’s reusable AI engineering assets — every engagement distilled into patterns, agents, and reference architectures the whole team benefits from. Key Responsibilities: PoC / PoV / MVP Delivery: • Take ownership of high-priority BU use cases from problem framing through to a working prototype, at days-to-weeks cadence, and form a defensible view on whether each should progress. • Ship first production versions directly for use cases where the path is clear — before the Sr. Solutions Engineer takes them forward to scale. AI-Native Engineering: • Operate AI agent workflows (planning, coding, testing, review, documentation) for autonomous prototype delivery — selecting the right level of automation for each engagement. • Use AI coding assistants as the default mode of building; provide judgement, course-correction, and validation. Customer Embedding & Handoff: • Embed with BU teams — attend standups, understand the domain, prototype against real data and real workflows. • Package proven use cases cleanly for the Sr. Solutions Engineer — working code, architecture rationale, open risks, and scaling concerns. Governance, Standards & Coaching: • Begin Code Orange, Responsible AI, and Accountability Report conversations during prototyping — so the productionisation team does not inherit governance debt. • Contribute reusable agents, prompt templates, and reference architectures to the organisation’s pattern library after each engagement. • Form and defend kill/continue recommendations at the end of each engagement — presenting clearly to BU and Enterprise AI leadership. • Coach BU engineers on AI-augmented ways of working while embedded, building BU capability alongside delivery outcomes. Required Functional Experience: • Overall 15+years of full-stack or AI/ML engineering experience, including production deployments. • Demonstrated ability to take ideas from sketch to working software at speed — startup, lab, or innovation-team background is ideal. • Strong proficiency in Python and at least one of TypeScript/JavaScript, Go, or similar. • Hands-on experience with GenAI pa