Senior Solution Support Engineer

Job description

About the Role: GSK’s Enterprise AI team is expanding its ability to architect, build, and run complex AI solutions across the enterprise. Senior Solutions Engineers sit at the intersection of technical architecture, hands-on AI engineering, and production operations — ensuring that AI capabilities translate reliably into production value and keep delivering it after go-live. As a Senior Solutions Engineer you will own the technical solution design for complex AI integrations and personally build and operate them, working across EAI engineering, business technology teams, external partners, and strategic suppliers to ensure that what gets built can be deployed, integrated, maintained, and operated at enterprise scale. This is a hands-on role: you will design, build, and review AI systems yourself — including LLM, agentic, and retrieval-based solutions — working within the architectural standards, engineering practices, and lifecycle framework set by the Staff Solutions Engineer, and contributing to how they evolve. You bring both the architectural depth to make the right design choices and the communication skills to earn confidence across technical and business audiences. The role carries direct accountability for the AI products and integrations you deliver. Expect to spend roughly 40% of your time on production operations and support — keeping the lights on (KTLO), meeting SLAs and service metrics, and day-to-day coordination with strategic suppliers — and roughly 60% on engineering, with a deliberate focus on using that engineering to automate operations and steadily reduce the operational load. You will use AI agents as part of your engineering and operations toolkit, supervising their output, to keep lowering the cost at which value is delivered for the solutions you own. As those solutions scale in deployment, you are responsible for identifying when the current engineered solution needs to be refactored and/or optimised, and for recommending and delivering that change — escalating portfolio-level cases to the Staff Solutions Engineer. Why This Role Exists: As the Enterprise AI portfolio matures, the complexity of integration, deployment, and operations grows. Many of the most valuable AI opportunities require deep technical solutioning before engineering can begin, hands-on engineering to build them, and disciplined operation once they are live. In this role you will be the person who: Closes the gap between AI capability and enterprise deployment readiness through rigorous technical solutioning. Enables faster delivery by resolving integration complexity early, before it becomes a production blocker. Builds and ships production AI solutions hands-on, so that architectural decisions are grounded in working code rather than slideware. Keeps products healthy in production by owning KTLO, SLAs, and service metrics for the solutions you deliver. Automates the run through monitoring, AIOps, and AI agents, so reliability improves and the cost to operate falls as the portfolio grows. Governs the lifecycle by applying Product Lifecycle Management (PLM) discipline — stage gates, versioning, change control, and traceability — to every AI product you deliver. Raises the bar on solution quality by applying and contributing to architectural standards across complex engagements. Builds trust with business technology partners and enterprise architects through credible, well-grounded technical guidance. Key Responsibilities: Solution Architecture & Technical Design Own technical solution design for complex AI integrations — from requirements through to detailed architecture, component selection, and integration specification. Define integration patterns for connecting AI capabilities with enterprise systems: SAP, Veeva, Salesforce, clinical data platforms, and GSK’s data infrastructure. Design for operability so that observability, SLOs, runbooks, and support requirements are built into the solution from the start rather than added after go-live. Produce solution architecture documentation that enables downstream engineering teams to build with confidence and minimises ambiguity at handoff. Hands-On AI Engineering (approx. 60% of role) Build and ship production AI solutions — including LLM-based applications, agentic workflows, retrieval-augmented generation (RAG), and ML model serving — integrated with GSK enterprise systems. Write production-quality code and reference implementations, with tests, evaluation harnesses, guardrails, and CI/CD for the AI systems you deliver. Apply the function’s AI engineering standards — prompt and model versioning, evaluation, secure deployment — and contribute improvements back to them. Evaluate models, frameworks, and tooling through hands-on benchmarking of quality, latency, cost, and risk, and recommend the right choice for each engagement. Partner with Forward Deployed Engineers and platform engineers to take proven prototypes through to hardened, supportable production services, and own the handoff for your engagements. Use AI agents and coding assistants as part of daily engineering and operations, supervising and quality-assuring their output to lower the cost of delivering the solutions you own. Deployment Readiness & Technical Assurance: Lead technical due diligence for complex deployments — identifying integration risks, data dependencies, security considerations, and operational requirements before build begins. Drive deployment readiness assessments ensuring AI solutions meet enterprise architecture standards, security policy, and regulatory requirements prior to production. Provide technical assurance throughout delivery — reviewing design decisions, flagging architectural drift, and escalating risks to engineering and product leadership. Product Lifecycle Management (PLM) for Enterprise AI: Apply the PLM framework and stage gates to every AI product and integration you deliver, preparing the release evidence — evaluation results, security, data privacy, regulatory and quality requirements. Maintain accurate lifecycle records for your products — models, versions, prompt versions, dependencies, owners, and status — and drive change management, deprecation, and retirement. Ensure traceability and auditability across the lifecycle (data lineage, model and prompt versions, approvals, and decisions), suitable for a regulated pharma environment. Feed delivery and operational experience back into the PLM framework so that gates and criteria improve over time. Production Operations & Support (approx. 40% of role): Own production operation and support for the AI integrations and products you deliver, including KTLO activities such as patching, upgrades, dependency and certificate management, and break-fix. Deliver against SLAs, SLOs, and service metrics (availability, latency, incident response and resolution times, change success rate, cost to serve) for your services, reporting performance to the Staff Solutions Engineer and business owners. Diagnose and resolve integration failures and lead incident response for your services, running post-incident reviews that drive root-cause fixes rather than repeated manual remediation. Work day to day with strategic suppliers and platform vendors on delivery — raising and tracking tickets, coordinating support escalations, and flagging performance or SLA issues to the Staff Solutions Engineer. Maintain runbooks and knowledge base articles for your services, and participate in the on-call and escalation model. Track operational toil for the services you own and convert it into automation. Intelligent Operations & Automation: Build and operate observability for your services — monitoring of model quality, drift, hallucination and safety signals, latency, availability, and cost — with defined SLOs, alerting, and runbooks (AIOps / LLMOps / MLOps). Apply AI and agents to operations itself — automated triage, root-cause analysis, runbook execution, cost and capacity optimisa