Staff Solution Support Engineer
GSK · Bengaluru
About the Role
GSK's Enterprise AI team is building an integration and operations capability that turns AI potential into enterprise-grade, production-ready deployment. The Staff Solutions Engineer leads the Solutions Engineering function — the architectural and engineering backbone that bridges AI capability and enterprise systems at scale, and keeps AI products healthy, governed, and improving after go-live.
As the Staff Support Solutions Engineer, you will lead the Solutions Engineering function within Enterprise AI, setting the architectural strategy, developing the team, and personally leading the highest-stakes integration programmes. This is a hands-on technical leadership role: you will design, build, and review AI systems yourself — including LLM, agentic, and retrieval-based solutions — and set the engineering bar for the function. Where the Senior Solutions Engineer owns complex individual engagements, the Staff operates at the portfolio level — ensuring integration patterns are reusable, architectural standards are consistent, every AI product is managed through a clear lifecycle, and the function scales to meet the demands of a rapidly maturing AI portfolio.
You will also own the vision for Intelligent Operations in Enterprise AI: the observability, automation, and AI-assisted operating model that lets a growing portfolio of AI products run reliably, safely, and cost-effectively at scale.
The role carries direct accountability for production. Expect to spend roughly 40% of your time owning production operations and support — keeping the lights on (KTLO), meeting SLAs and service metrics, and managing strategic supplier delivery — and roughly 60% on engineering, with a deliberate focus on using that engineering to automate operations and steadily reduce the operational load.
You will direct a fleet of AI agents and Senior Solutions Engineers to continuously improve the cost at which value is delivered. As applications scale in deployment, you are responsible for identifying when the current engineered solution needs to be refactored and/or optimized, and for leading that change.
Why This Role Exists
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 recognize when solutions must be refactored or optimized 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.
Key Responsibilities:
Function Leadership & Team Development:
- 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.
Hands-On AI and Product Engineering:
- Dedicate the majority of your time to engineering, with a primary focus on building automation, tooling, and AI-assisted capabilities that reduce manual operations and improve reliability of the production portfolio.
- Personally design, 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.
- Lead by example in code: author reference implementations, conduct deep technical reviews, and prototype new patterns before they are standardized.
- Establish engineering standards for AI systems, including prompt and model versioning, evaluation harnesses, guardrails, testing, CI/CD for AI, and secure deployment.
- Evaluate and select models, frameworks, and tooling based on hands-on benchmarking of quality, latency, cost, and risk.
- Partner with Forward Deployed Engineers and platform engineers to take proven prototypes through to hardened, supportable production services.
Portfolio Architecture & Standards:
- Own the architectural strategy for AI integration across the Enterprise AI portfolio — defining where the function is heading and what standards it operates to.
- Define and maintain canonical integration patterns, architecture decision records, and design standards that enable faster, lower-risk delivery across all engagements.
- Identify where investment in common patterns, tooling, or platform capabilities will compound across multiple engagements, and make the case for that investment.
- Drive architectural coherence across the portfolio — ensuring individual engagement decisions do not create technical debt or fragmentation at scale.
Product Lifecycle Management (PLM) for Enterprise AI:
- Establish stage gates and release criteria — covering evaluation results, security, data privacy, regulatory and quality requirements — that balance speed with enterprise and regulatory risk.
- Maintain a portfolio-level inventory of AI products, models, versions, dependencies, owners, and lifecycle status, with clear processes for 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.
- Integrate PLM with enterprise architecture, security, quality, and compliance processes so governance is built into delivery rather than added afterwards.
Intelligent Operations for Enterprise AI:
- Design and deliver the Intelligent Operations model for the Enterprise AI organisation: end-to-end observability, automated incident detection and response, and continuous improvement of deployed AI products.
- Implement AIOps and LLMOps/MLOps capabilities — monitoring of model quality, drift, hallucination and safety signals, latency, availability, and cost — with defined SLOs, alerting, and runbooks.
- Apply AI and agents to operations itself, such as automated triage, root-cause analysis, runbook execution, capacity and cost optimisation, and self-service support for product teams.
- Establish feedback loops from production usage and operational data into design, evaluation, and PLM decisions, so the portfolio improves with every release.
- Define operating-model standards — ownership, on-call, escalation, support tiers, and the FDE-to-SE handoff — so products are scaled and operated consistently.
Intelligent Operations & Support:
- Own production operation and support for Enterprise AI products and integrations, including KTLO (keep-the-lights-on) activities such as patching, upgrades, dependency and certificate management, capacity, and break-fix.
- Be accountable for SLAs, SLOs, and service metrics (availability, latency, incident response and resolution times, change success rate, cost to serve), with clear reporting to Enterprise AI leadership and business owners.
- Lead major incident response and post-incident reviews, driving root-cause fixes and preventative engineering rather than repeated manual remediation.
- Work with strategic suppliers and platform vendors on delivery: manage performance against contracts and SLAs, coordinate support escalations, align roadmaps, and hold suppliers accountable for quality, timelines, and service levels.
- Define and run the support model — on-call, escalation paths, support tiers, runbooks, and knowledge base — and manage the operational backlog alongside planned engineering work.
- Track and reduce operational toil: measure the time the team spends on KTLO and manual operations and set targets to convert that effort into automation.
Agent-Led Delivery, Cost-to-Value & Solution Evolution:
- Direct a fleet of AI agents and Senior Solutions Engineers — setting objectives, guardrails, and review standards — to continuously improve the cost at which value is delivered across the Enterprise AI portfolio.
- Define and track unit-economics and value metrics (for example cost per transaction or outcome, run cost against business value, and engineering and operational effort per product), and use them to prioritize optimization work.
- Orchestrate, supervise, and quality-assure agent-driven engineering and operations work such as code generation, testing, triage, and remediation, keeping human review in place where risk requires it.
- As applications scale in deployment, take responsibility for identifying when the current engineered solution needs to be refactored and/or optimized — for performance, reliability, cost, or maintainability — and make the case, plan the work, and lead the change before technical debt or cost compounds.
- Monitor scale signals (usage growth, latency, cost per unit, incident trends, architectural limits) so refactor and optimization decisions are triggered early and feed the outcomes back into canonical patterns and PLM.
Strategic Delivery:
- Take personal ownership of the highest-stakes integrations — those that set precedents for the function, involve novel architectural territory, or carry significant enterprise risk.
- Lead technical assurance for major programmes involving multiple integration workstreams, ensuring design decisions are sound, well-documented, and reusable.
- Make and defend architectural direction decisions with multi-year consequences for the integration portfolio.
Cross-Functional Influence & Partner Engagement:
- Represent the Solutions Engineering function in Enterprise AI leadership forums, shaping cross-function delivery standards and platform strategy.
- Own relationships with GSK enterprise architecture, security, and senior business technology leadership — building institutional trust and accelerating complex approvals.
- Influence EAI platform engineering decisions based on integration, deployment, and operational experience at portfolio scale.
- Partner with the Forward Deployed Engineering function to define and refine handoff standards between FDE (build and prove) and SE (scale and operate).
Required Experience:
- 18+ 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 standardizing 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 recognizing when systems must be refactored or optimized as they scale, and leading that work (performance tuning, re-architecture, cost optimization) 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 Experience:
- 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 organizational 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 organizational 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).
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