Staff Solution Support Enginner

Job Purpose : As the Enterprise AI portfolio scales, the complexity of integration and operations grows faster than individual Senior Solutions Engineers can absorb. A Staff-level leader is needed to: • Raise the architectural ceiling across the portfolio by converting individual engagement experience into reusable patterns and standards that reduce cost and risk at scale. • Lead the most novel and precedent-setting integrations — those involving architectural territory the function has not navigated before — and stay close enough to the code to make authoritative engineering decisions. • Establish Product Lifecycle Management (PLM) for Enterprise AI, so every AI product and model is governed consistently from ideation through scale, operation, and retirement. • Deliver Intelligent Operations for the Enterprise AI organization, using monitoring, automation, and AI-driven tooling to run the portfolio with fewer incidents, faster recovery, and lower cost to serve. • Own production operations for Enterprise AI — KTLO, SLA and service-metric commitments, and strategic supplier delivery — while engineering automation that shrinks the operational burden as the portfolio grows. • Direct a fleet of AI agents and Senior Engineers to continuously improve the cost at which value is delivered, and recognise when solutions must be refactored or optimised as deployments scale. • Build and develop the Solutions Engineering team, creating a function that grows in capability alongside the AI portfolio. • Represent EAI's integration architecture with senior GSK technology and enterprise architecture leadership, building institutional credibility and accelerating complex approvals. Responsibilities: • Lead the Solutions Engineering function — strategy, team structure, hiring, development, and standards. • Hire, mentor, and develop Senior Solutions Engineers, building clear development paths with active coaching and regular feedback. • Set the architectural and AI engineering standards and integration patterns the whole function operates to, ensuring consistency and quality across all engagements. • Build and maintain a culture of rigorous documentation, code quality, and knowledge sharing within the SE function. Required skills: • 16+ years in enterprise solution architecture, technical engineering, or equivalent, with significant time leading solutions engineering or architecture functions. • Recent, hands-on AI engineering experience: you have personally built and deployed production AI/ML or LLM-based systems (for example RAG, agentic workflows, model serving, evaluation pipelines) and can write, review, and debug code in languages such as Python. • Experience directing teams of engineers and AI agents (agentic engineering and operations workflows) to deliver at lower cost, with the ability to measure and improve cost against delivered value. • Deep expertise in enterprise integration patterns — APIs, event-driven architectures, data exchange standards — with a track record of standardising at portfolio scale. • Practical experience with MLOps/LLMOps and cloud-native deployment (CI/CD, containers, infrastructure as code, observability) sufficient to make authoritative decisions about deployment, integration, and operability at scale. • Experience designing or operating Product Lifecycle Management (PLM) or equivalent lifecycle governance for technology, data, or AI products, including stage gates, versioning, change control, and retirement. • Demonstrated ability to deliver intelligent operations: building monitoring, automation, and AIOps capabilities that improve reliability, reduce operational toil, and control cost for production platforms or AI services. • Experience owning production operations and support for enterpr