AI Platform Engineer II

Job Purpose : As Enterprise AI’s delivery portfolio grows and GenAI and agentic solutions move from pilot to production, the platform must scale reliably and safely. The AI Platform Engineer owns significant areas of that platform — ensuring they operate at SLO, building automation against routine operations work, embedding security and responsible AI controls into the platform layer, and contributing to the vendor and standards conversations that shape the platform’s direction. Owns significant platform areas with genuine accountability for reliability, security, performance, and cost efficiency of AI services — not just task execution. Makes AI consumable at enterprise scale by operating the gateways, model endpoints, and context services through which every team reaches models, tools, and data. Builds platform leverage through automation, self-service, and reusable components that reduce manual operations work across the team. Embeds governance by design so that AI security, data protection, and responsible AI controls are enforced by the platform rather than negotiated project by project. Develops the next generation by mentoring junior and mid-level platform engineers, building team capability over time. Responsibilities : At least 6 years of experience running SaaS, cloud, or platform-engineering services in a production setting, including at least 2 years operating AI/ML or GenAI workloads or services. Strong proficiency in Python; working proficiency in at least one additional language (for example, Go, TypeScript, or Java). Hands-on experience with Kubernetes and container platforms, Infrastructure-as-Code (Terraform; Bicep a plus), GitOps, and modern CI/CD. Hands-on experience operating API gateways or AI/LLM gateways, including routing, rate limiting, authentication, and policy enforcement for AI traffic. Working knowledge of Model Context Protocol (MCP) and agentic patterns — tool calling, orchestration, and agent frameworks — and of how context is retrieved, managed, and governed for LLM applications. Experience operating production services with defined SLOs, observability, and incident management. Daily use of AI coding agents (Claude Code, GitHub Copilot, Databricks AI Assistant) as a primary development mode. Solid understanding of identity and access management, secrets management, and data protection for cloud and AI services. Required skills: Platform & Cloud: Kubernetes, containers, Terraform, GitOps, CI/CD, DevSecOps, platform reliability engineering, and at least one major cloud (Azure preferred) with multi-cloud and hybrid architecture awareness. AI Gateway & API Management: API gateway patterns, AI/LLM gateway architecture and operations, traffic management, and policy enforcement. GenAI Operations: LLM deployment, versioning, and monitoring; prompt and evaluation workflows; RAG architectures; vector search fundamentals. MCP & Agents: Model Context Protocol, tool calling, workflow orchestration, and an understanding of agent orchestration frameworks and context engineering. Observability: Metrics, logging, tracing, and alerting (for example, OpenTelemetry, Prometheus, Grafana, or cloud-native equivalents) applied to AI services. Security: IAM, OAuth/OIDC, secrets management, network security, and data privacy controls for AI workloads. Education : Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience. Master’s degree in Computer Science, Distributed Systems, or SRE is advantageous. Preferred skills: Azure AI services (Azure OpenAI/AI Foundry, Azure API Management), with AWS and GCP AI services (for example, Bedrock, Google Cloud AI platform) desired. Model serving and GPU infrastructure: inference runtimes (for example, vLLM, KServe, Triton), GPU scheduling, and autoscaling. Vector and retrieval stacks: vector databases and hybrid search (for example, pgvector, Azure AI Search, OpenSearch, or dedicated vector stores). Agent and LLM frameworks: LangChain/LangGraph, Microsoft Agent Framework, or comparable; agent interoperability protocols; evaluation tooling. Gateway technologies: Kong AI Gateway or comparable (for example, Envoy-based gateways, LiteLLM, Portkey, Azure API Management). Platform tooling: Databricks, policy-as-code (OPA/Kyverno), service mesh, internal developer platforms (for example, Backstage), and FinOps for AI (token economics and unit-cost reporting). AI security & resilience: guardrail frameworks, AI red-teaming, and resilience testing of AI services #AGTech Skills AI Systems, Azure OpenAI Service, Google Cloud Document AI, Identity Access Management (IAM) 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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