Staff Solution Support Engineer

Job Summary

• 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 EAIs integration architecture with senior GSK technology and enterprise architecture leadership, building institutional credibility and accelerating complex approvals.

• 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.

• 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 enterprise platforms or AI services, including KTLO, SLA/SLO and service-metric accountability, incident and problem management, and on-call and escalation models.

• Track record of recognising when systems must be refactored or optimised as they scale, and leading that work (performance tuning, re-architecture, cost optimisation) in production.

• Experience managing strategic suppliers or vendors on delivery, including performance reviews, SLA enforcement, escalation, and roadmap alignment.

• Proven ability to build and develop technical teams: hiring, mentoring, setting standards, and creating clear development paths.

• Confidence engaging at senior executive level: representing complex integration strategy to GSK technology leadership, enterprise architecture, and senior business stakeholders.

• Experience operating in a regulated enterprise environment (pharma, healthcare, or financial services preferred).

Preferred Skills

• Experience leading a Solutions Engineering or technical architecture function at a company with a complex enterprise integration landscape.

• Demonstrated track record of building canonical integration patterns adopted at organisational scale.

• Prior experience in an AI-native company (Anthropic, Palantir, OpenAI, or similar) or equivalent AI-first engineering environment.

• Experience setting AI engineering standards, responsible AI practices, and architectural governance at an organisational level.

• Familiarity with ITIL or SRE practices (error budgets, blameless post-incident reviews, change management) applied to AI and data platforms.

• Familiarity with GxP, computer system validation, or other quality and regulatory frameworks as they apply to AI and software products.

• Experience with agent frameworks, vector and graph data stores, model evaluation and red-teaming, and AI cost management (FinOps).

Skills

• AI Platforms
• AI Systems
• Data Analysis
• LLMOps
• ML Platforms
• MLPs
• Product Lifecycle Management (PLM)
• Product Lifecycle Management (PLM) Systems
• Product Roadmapping
• Roadmap Development
• Statistical Analysis

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.