Staff - Forward Deployed Engineer

GSK•Philadelphia, PA

About The Position

GSK’s Enterprise AI team builds AI solutions that prove value quickly and scale to production. Forward Deployed Engineering (FDE) is the front of the delivery pipeline: working with business units to prove AI ideas through PoCs, PoVs, MVPs, and, where appropriate, initial production versions, before handing proven solutions to Solutions Engineering for scale-up. This hands-on leadership role leads the FDE function, including its strategy, engagement priorities, team structure, hiring, development, and standards. The role manages a flat team of Lead, Senior, and Forward Deployed Engineers; owns relationships with Director- and Senior Director-level business sponsors; and represents FDE to GSK leadership. The successful candidate will continue contributing technically to the most strategic engagements while building a team that delivers quickly and develops toward greater seniority. The role sets AI-native engineering practices and AI, machine learning, and data science standards; makes portfolio-level decisions about continuing or stopping engagements; coordinates with Solutions Engineering and Operations Engineering; influences vendor and tooling choices; and ensures governance is built into work from the prototype stage. It reports on pipeline, outcomes, team development, and capacity to Enterprise AI leadership.

Requirements

12 or more years of experience in software engineering and AI/ML engineering, including significant time leading senior engineering teams or functions.
Bachelor’s degree in computer science or software engineering, or equivalent practical experience.
Proven track record of taking AI ideas from concept to working software quickly in customer-facing engineering roles.
Strong technical depth in generative AI patterns, including agents, multi-agent orchestration, retrieval-augmented generation (RAG), and evaluation.
Recognized expertise across AI, ML, and data science, including classical ML, deep learning, LLMs and foundation models, statistical and causal inference, and MLOps at scale; able to evaluate and challenge modeling decisions across engagements.
Demonstrated daily use of AI coding agents, such as Claude Code, GitHub Copilot, or Databricks AI Assistant, as a primary development mode; experience designing agentic delivery-automation workflows and delivery bot meshes and establishing team standards for their use.
Experience hiring, mentoring, and developing senior engineering talent and building an engineering function’s structure, standards, culture, and ways of working.
Track record of representing engineering functions to senior business stakeholders.

Nice To Haves

Prior leadership experience at an AI-native company such as Anthropic, Palantir, or OpenAI, in a role such as Head of Forward Deployed Engineering, Director of Solutions, or equivalent.
Master’s degree in computer science or a related field.
Experience in regulated industries such as pharma, healthcare, or financial services.
Experience setting AI engineering standards at an organizational level.

Responsibilities

Lead the Forward Deployed Engineering function, owning its strategy, structure, hiring, development, standards, and engagement pipeline.
Hire, mentor, and develop the FDE team, establishing development paths and milestones from G8 through G5 and providing active coaching.
Set and document standards for AI-native engineering practices, including agentic delivery-automation workflows, delivery bot meshes, prompt and pattern libraries, and handoff practices.
Set standards for AI, ML, and data science rigor, including evaluation, experimental design, and model validation; serve as the final technical authority on modeling approaches for strategic engagements.
Own relationships with Director- and Senior Director-level business unit sponsors, bringing use cases to Enterprise AI and sustaining engagement through delivery.
Personally lead the most strategic FDE engagements and remain hands-on in engagements that establish precedents for the function.
Make and defend portfolio-level decisions to continue or stop FDE engagements with business unit and Enterprise AI leadership, translating engineering possibilities into business outcomes.
Track AI/ML research and translate advances into practical team guidance.
Coordinate with Solutions Engineering and Operations Engineering on the delivery pipeline and represent FDE in Enterprise AI leadership forums.
Influence vendor strategy and tooling choices for AI-native engineering within Enterprise AI.
Build governance into FDE work from the prototype stage, including Code Orange, Responsible AI, and Accountability Report governance.
Report to Enterprise AI leadership on FDE performance, including engagement pipeline, outcomes, team development, and capacity.