Engineer I - Forward Deployed Engineer

Business Introduction

GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver £40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.
Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.
Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.

Job Purpose

GSK’s Enterprise AI team builds AI solutions that prove value fast and scale to production. Forward Deployed Engineers are the builders at the front of the delivery pipeline — embedded with business unit teams, prototyping AI ideas at a days-to-weeks cadence, and handing proven solutions off cleanly for scale-up.

The Engineer I is a mid-level individual contributor in the FDE team. This role contributes to FDE engagements under the direction of more senior FDEs, learning the practice through hands-on work. Most prototyping is AI-augmented: AI coding assistants and multi-agent workflows handle the bulk of routine code, and learning to use them fluently is part of the role. The FDE does not carry use cases to long-term operations — clean handoff is part of the job.

Why This Role Exists

Enterprise AI needs engineers who can contribute to embedded prototype work alongside domain experts. The role builds the pipeline of senior FDE capability the function needs as its portfolio grows.

• Contributes to prototyping under the direction of Lead and Senior FDEs — shipping working AI proofs of concept with BU teams.

• Builds AI-native fluency using AI coding assistants and multi-agent workflows as the default mode of work.

• Learns the FDE practice from more senior team members through hands-on engagements and direct mentorship.

Key Responsibilities

This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:

FDE Engagement Contribution

• Contribute to BU use case engagements — prototyping, demoing, refining — under the direction of more senior FDEs.

• Embed with BU teams alongside the Lead or Senior FDE, building working relationships with BU engineers and product owners.

AI-Native Engineering

• Operate AI agent workflows under guidance — contributing to multi-agent prototype delivery.

• Use AI coding assistants as the default mode of building; develop genuine fluency with Claude Code, GitHub Copilot, and Databricks AI Assistant. Go beyond vibe coding: contribute to building agentic workflows that automate delivery, and work with delivery bot meshes as part of day-to-day engineering.

Delivery & Handoff

• Contribute to clean handoff packages for the Solutions Engineer — documentation, runbooks, and working software.

• Apply existing patterns from the FDE pattern library; contribute small refinements to patterns as experience develops.

Governance & Learning

• Support Code Orange, Responsible AI, and Accountability Report conversations during prototyping alongside more senior FDEs.

• Actively learn the FDE practice from more senior team members; seek feedback and apply it rapidly.

Why You?

Basic Qualification

We are seeking professionals with the following required skills and qualifications to help us achieve our goals:

• 1+ years of experience in full-stack software engineering, AI engineering, machine learning engineering, or a related field.
• Experience programming in Python and at least one additional programming language, such as TypeScript, JavaScript, Go, or a similar language.

• Experience developing generative AI solutions using one or more of the following: large language models, agents, multi-agent orchestration, retrieval-augmented generation (RAG), or prompt engineering.

• Experience applying AI, machine learning, or data science methods, such as supervised or unsupervised learning, model training, model evaluation, feature engineering, or data analysis.

Preferred Qualification

If you have the following characteristics, it would be a plus:

• Experience in a customer-facing, embedded engineering, startup, laboratory, consulting, or innovation-team environment.
• Experience building agentic workflows, developer automation, or multi-agent delivery systems.

• Experience working in a regulated industry, such as biopharma, healthcare, financial services, or another environment with formal governance requirements.

• Experience contributing to reusable engineering patterns, shared libraries, templates, or internal developer platform.

What Success Looks Like

In the first 12 months, the person in this role will have:

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