Engineer II

GSK's Enterprise AI team builds AI solutions that prove value fast and scale to production. The Engineer II is a senior individual contributor who takes full ownership of FDE engagements end-to-end , embedding inside business unit teams, prototyping AI ideas at a days-to-weeks cadence, leading G8 FDEs on multi-engineer engagements, and handing proven solutions off cleanly to the Solutions Engineer.

This role operates at the intersection of technical depth and stakeholder influence. The role does not just execute engagements , they help qualify which BU problems are worth tackling, shape engagement standards, and actively mentor the G8 FDEs alongside them. Most prototyping is AI-augmented: AI coding assistants and multi-agent workflows handle the bulk of routine coding, freeing the Senior FDE to focus on framing, judgement, and validation.

Key Responsibilities:

• Take full ownership of BU use cases from problem framing through to working prototype, at days-to-weeks cadence, without close supervision.
• Qualify incoming BU asks , challenge underspecified problems, assess feasibility, and define scope before committing to an engagement.
• Embed with BU teams , product owners, engineering leads, and the wider BU , building trusted working relationships and genuine domain understanding.
• Form and defend kill/continue recommendations at the end of each engagement based on evidence , presenting clearly to BU and Enterprise AI leadership.
• Ship first production versions on engagements where the path is clear and complexity is bounded, before handing forward to the Solutions Engineer.

Team Leadership & Mentoring:

• Lead small multi-FDE engagements , direct the work of G8 FDEs, review their output, and ensure delivery quality without becoming the bottleneck.
• Actively mentor G8 FDEs during engagements: provide technical guidance, share engagement judgement, and give structured feedback that accelerates their development.

AI-Native Engineering:

• Operate AI agent workflows for autonomous prototype delivery , orchestrating multi-agent systems as the primary engineering mode.
• Use AI coding assistants as the default mode of building; direct, validate, and course-correct AI-generated code at engineering speed.

Delivery & Handoff:

• Package proven use cases cleanly for the Solutions Engineer , working code, architecture rationale, open risks, and scaling concerns.
• Own the quality of FDE pattern library contributions , distill every engagement into reusable agents, prompt templates, and reference architectures, and ensure the bar is high enough that others can adopt without rework.

Requirements:

• Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
• 3+ 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.
• Experience developing generative AI solutions using one or more of the following: agents, multi-agent orchestration, retrieval-augmented generation (RAG), prompt engineering, or evaluation.
• Experience designing, training, evaluating, or tuning machine learning or deep-learning models using frameworks such as PyTorch or TensorFlow.