Senior Solution Support Engineer

Overview

Senior Solutions Engineer within GSK's Enterprise AI team, owning technical solution design for complex AI integrations and personally building and operating them, with roughly 40% on production operations and support and 60% on hands-on engineering.

What you'll do

• Own technical solution design for complex AI integrations, from requirements through detailed architecture, component selection, and integration specification.
• Define integration patterns connecting AI capabilities with enterprise systems including SAP, Veeva, Salesforce, clinical data platforms, and GSK's data infrastructure.
• Design for operability, building observability, SLOs, runbooks, and support requirements into solutions from the start.
• Produce solution architecture documentation that enables downstream engineering teams to build with confidence.
• 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 delivered.
• Apply the function's AI engineering standards, including prompt and model versioning, evaluation, and secure deployment, and contribute improvements back to them.
• Evaluate models, frameworks, and tooling through hands-on benchmarking of quality, latency, cost, and risk.
• Partner with Forward Deployed Engineers and platform engineers to take prototypes through to hardened production services.
• Lead technical due diligence for complex deployments, identifying integration risks, data dependencies, security considerations, and operational requirements.
• Drive deployment readiness assessments ensuring solutions meet enterprise architecture standards, security policy, and regulatory requirements.
• Apply the PLM framework and stage gates to every AI product and integration, maintaining lifecycle records, versioning, change control, and traceability.
• Own production operation and support for delivered AI integrations, 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) and report performance to stakeholders.
• Diagnose and resolve integration failures, lead incident response, and run post-incident reviews driving root-cause fixes.
• Coordinate day-to-day with strategic suppliers and platform vendors, including ticket management and SLA escalations.
• Build and operate observability for services, including monitoring of model quality, drift, hallucination and safety signals, latency, availability, and cost.
• Apply AI and agents to operations, including automated triage, root-cause analysis, runbook execution, and cost and capacity optimisation.
• Track unit economics and value metrics for owned solutions, and identify when engineered solutions need refactoring or optimisation as they scale.

What you'll need

• 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 experience personally building and deploying production AI/ML or LLM-based systems (for example RAG, agentic workflows, model serving, evaluation pipelines).
• Ability to 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).
• 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.
• Experience operating in a regulated enterprise environment (pharma, healthcare, or financial services preferred).
• Confidence engaging with senior technical and business stakeholders, running workshops, producing architecture artefacts, and defending design choices.

Nice to have

• 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).

Details

• Location: Bengaluru Luxor North Tower.