Senior, Forward Deployed Engineer (Digital Twin & Optimization)

Senior Forward Deployed Engineer at GSK Enterprises AI embeds with BU teams to rapidly prototype AI-enabled solutions. You will lead PoCs/PoVs/MVPs, deliver production-ready prototypes, and hand off scalable solutions to the Sr. Solutions Engineer. You’ll work on in-silico process models, digital twins, and optimization, shaping AI-enabled process development for molecules. Expect hands-on engineering, mentoring peers, and close collaboration with scientists and modelers to deliver real patient impact.

• hybrid work pattern (UK)
• learning and development opportunities
• impactful healthcare outcomes
• inclusive culture
• supportive team environment
• career growth in AI & engineering

• Own high-priority BU use cases from framing to working prototype in days-to-weeks
• Ship production-ready MVPs for clear paths to scale
• Operate AI agent workflows for autonomous prototype delivery with appropriate automation
• Embed with BU teams, prototype against real data, and hand off with architecture rationale and risks
• Contribute reusable agents, templates, and reference architectures to the pattern library
• Define kill/continue decisions and defend them to BU and Enterprise AI leadership
• Coach BU engineers on AI-augmented ways of working and build BU capability

• Strong full-stack or AI/ML engineering experience with production deployments
• Proficiency in Python and at least one of TypeScript/JavaScript, Go, or similar
• Solid understanding of process modeling approaches (mechanistic, data-driven, hybrid) and ability to work with process modelers
• Strong AI modeling skills including model identification and calibration for surrogate models, AutoML, and agent-assisted modeling
• Proven ability to deliver a digital twin or optimization MVP end-to-end
• Experience in optimization and scenario planning (linear/mixed-integer programming, solvers, scheduling) and simulation-based what-if analysis
• Hands-on with GenAI patterns (agents, multi-agent orchestration) and MLOps basics to ship MVPs
• Daily use of AI coding assistants (e.g., Claude Code, GitHub Copilot, Databricks AI Assistant)

• Collaborative and coach-like mindset
• Strong judgment and ability to frame and validate uncertain problems
• Clear, practitioner-focused communication with cross-functional teams
• AI/ML engineering for production
• Python
• TypeScript/JavaScript or Go