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 optimisation, and self-service support for product teams.
Feed production usage and operational data back into design, evaluation, and PLM decisions so the portfolio improves with every release.
Contribute to operating-model standards — ownership, support tiers, escalation, and the FDE-to-SE handoff — drawing on what you learn running real services.
Solution Evolution & Cost-to-Value
Track unit-economics and value metrics for the solutions you own (for example cost per transaction or outcome, and run cost against business value) and use them to prioritise optimisation work.
As your solutions scale in deployment, identify when the current engineered solution needs to be refactored and/or optimised — for performance, reliability, cost, or maintainability — and make the case, plan the work, and deliver the change; escalate portfolio-level cases to the Staff Solutions Engineer.
Monitor scale signals (usage growth, latency, cost per unit, incident trends, architectural limits) so that refactor and optimisation decisions are triggered early, and feed the outcomes into reusable patterns.
Own the technical roadmap for integration capabilities, identifying where investment in common patterns will reduce cost and risk across the portfolio.
Contribute to platform engineering decisions based on deployment and integration experience — feeding real-world constraints back into how the platform evolves.
Partner Engagement & Technical Governance
Represent EAI’s technical architecture in conversations with enterprise architecture, data governance, security, and vendor partner teams.
Build and maintain relationships with business technology leads across GSK to support alignment and accelerate approval of complex deployments.
Contribute to EAI’s technical standards and architecture decision records, ensuring decisions are well-reasoned, documented, and reusable.
Required Experience
15+ years in enterprise solution architecture, software or AI engineering, or technical pre-sales/post-sales engineering, with a track record of designing complex integrations.
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.
Strong working knowledge of enterprise integration patterns, APIs, event-driven architectures, and data exchange standards.
Practical experience with MLOps/LLMOps and cloud-native deployment (CI/CD, containers, infrastructure as code, observability) sufficient to make sound decisions about AI deployment, integration, and operability.
Experience supporting production operations for enterprise platforms or AI services, including KTLO, SLA/SLO and service-metric delivery, incident and problem management, and on-call or escalation models.
Experience using automation — and ideally AI agents — to reduce operational toil and the cost of delivery or operation.
Working knowledge of Product Lifecycle Management (PLM) or equivalent lifecycle governance for technology, data, or AI products, including stage gates, versioning, change control, and retirement.
Experience working with vendors or suppliers on delivery, including SLA follow-up, support escalations, and performance conversations.
Track record of recognising when a system needs to be refactored or optimised as it scales, and delivering that work (performance tuning, re-architecture, cost optimisation).
Experience operating in a regulated enterprise environment (pharma, healthcare, or financial services preferred).
Confidence engaging with senior technical and business stakeholders: running workshops, producing crisp architecture artefacts, and defending design choices.
Preferred Experience
Experience in an AI-native company or equivalent AI-first engineering environment.
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).
#AGTech
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Skills
AI Frameworks, Algorithm Optimization, Artificial Neural Networks (ANNS), Computer Programming, Deep Learning, Google Cloud Platform (GCP) for Machine Learning, Machine Learning (ML), Machine Learning Algorithms, Microsoft Azure, ML Frameworks, Model Evaluation, Model Training, Performance Optimizations, Production Operations, Product Lifecycle Management (PLM), Solution Architecture
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
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