Staff Solution Support Engineer

Overview

Lead the Solutions Engineering function within GSK's Enterprise AI team, setting architectural strategy, developing the team, and personally leading the highest-stakes AI integration programmes while owning production operations and support.

What you'll do

• Lead the Solutions Engineering function, including strategy, team structure, hiring, development, and standards.
• Hire, mentor, and develop Senior Solutions Engineers with clear development paths.
• Set architectural and AI engineering standards and integration patterns for the function.
• Design, build, and ship production AI solutions, including LLM applications, agentic workflows, RAG, and ML model serving, integrated with GSK enterprise systems.
• Own the architectural strategy for AI integration across the Enterprise AI portfolio, including canonical integration patterns and architecture decision records.
• Establish Product Lifecycle Management for Enterprise AI, including stage gates, release criteria, portfolio inventory, and traceability.
• Design and deliver the Intelligent Operations model, including observability, AIOps and LLMOps/MLOps capabilities, and automated incident response.
• Own production operations and support, including KTLO, SLAs, SLOs, and service metrics.
• Lead major incident response and post-incident reviews.
• Manage strategic suppliers and platform vendors on delivery and SLA performance.
• Direct AI agents and Senior Solutions Engineers to improve the cost at which value is delivered.
• Identify when solutions need refactoring or optimization as deployments scale, and lead that change.
• Represent the Solutions Engineering function in Enterprise AI leadership forums and with GSK enterprise architecture, security, and business technology leadership.
• Partner with Forward Deployed Engineering to define handoff standards between build and operate.

What you'll need

• 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 building and deploying production AI/ML or LLM-based systems, such as RAG, agentic workflows, model serving, or evaluation pipelines.
• Ability to write, review, and debug code in languages such as Python.
• Experience directing teams of engineers and AI agents to deliver at lower cost, with the ability to measure and improve cost against delivered value.
• Deep expertise in enterprise integration patterns, including APIs, event-driven architectures, and data exchange standards, with a track record of standardizing at portfolio scale.
• Practical experience with MLOps/LLMOps and cloud-native deployment, including CI/CD, containers, infrastructure as code, and observability.
• Experience designing or operating Product Lifecycle Management 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.
• 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 in production.
• Experience managing strategic suppliers or vendors on delivery, including performance reviews, SLA enforcement, and escalation.
• Proven ability to build and develop technical teams, including hiring, mentoring, and setting standards.
• Confidence engaging at senior executive level on complex integration strategy.
• Experience operating in a regulated enterprise environment (pharma, healthcare, or financial services preferred).

Nice to have

• Experience leading a Solutions Engineering or technical architecture function at a company with a complex enterprise integration landscape.
• 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).

Details

• Location: Bengaluru, India.
• Time allocation: roughly 40% production operations and support, roughly 60% engineering.