Staff Solution Support Enginner
GSK is hiring a Staff Solution Support Enginner. Level rates it ; you can apply on GSK.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions OpenAI, Anthropic and RAG among its tools and skills.
- Level classes it as a lead role.
- GSK has 952 other open roles on Level.
- Posted in the last few days.
AI in this role
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 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).
#AGTech
Skills
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.
Inclusion at GSK:
As an employer committed to Inclusion, we encourage you to reach out if you need any adjustments during the recruitment process.
Please contact our Recruitment Team at IN.recruitment-adjustments@gsk.com to discuss your needs.
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How we rate this
Staff Solution Support Enginner at GSK rates 68 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, ML Ops, AI Evaluation, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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