Lead Customer Engineer, AI Infrastructure and Modernisation, UKI

About the job

When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.

Customers who've chosen Google Cloud want to know which machines and accelerators fit their workloads, how to run them well, and how to keep them busy. Migration and modernisation remain a large part of the job; AI infrastructure is where the hard questions land. You will own the technical relationship with platform teams, engineers and CTOs: some days white-boarding multi-host inference on GKE, some days telling a customer, politely, that their plan won't work and showing one that will. Customer Engineering at Google expects fluency in agentic development: you build with Gemini, Antigravity and the Agent Development Kit (ADK). If you haven't done this yet, you will need to want to. Much of the job is storytelling: making the complicated clear to whoever decides.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Own the technical side of AI infrastructure opportunities in UKI: qualify the workload, design and size the architecture, prove it, and stay close through to ramp.
• Help customers choose between, and get the most out of, TPUs and GPUs on GKE and Compute Engine, including scheduling, capacity and consumption options (reservations, Dynamic Workload Scheduler, Spot) and the serving stack on top.
• Guide customers through assessments of their existing environments and produce a prioritised roadmap for modernisation and migration to Google Cloud.
• Work directly with product management and engineering to get customer blockers removed, and bring field evidence into roadmap conversations.
• Build and run demos, workshops and proofs-of-concept, using agentic tooling as a matter of course, and contribute what you build back as reusable assets for the UKI and SSA field.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 10 years of experience with cloud native architecture in a customer-facing pre-sales role.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with AI accelerators (TPUs, GPUs) and the frameworks and serving stacks that run on them (e.g. PyTorch, JAX, vLLM, Ray).

Preferred qualifications:

• Experience designing, sizing and operating training or inference environments at scale: accelerator selection, cluster topology, storage and networking for AI workloads, and capacity planning.
• Experience running AI workloads on Kubernetes, including scheduling, autoscaling, node pool, compute class design, inference gateways and multi-host serving.
• Experience using agentic tools (Gemini, Antigravity, ADK or equivalents) in day-to-day engineering.
• Experience with security, identity and networking fundamentals on a hyperscaler: encryption, IAM, VPCs, load balancing, firewalls.
• Experience building and maintaining customer relationships.
• Knowledge of enterprise infrastructure modernisation, including VMware estates, backup and disaster recovery, and the realities of moving long-lived systems to cloud.

• Bachelor's degree or equivalent practical experience.
  
• 10 years of experience with cloud native architecture in a customer-facing pre-sales role.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with AI accelerators (TPUs, GPUs) and the frameworks and serving stacks that run on them (e.g. PyTorch, JAX, vLLM, Ray).