Customer Engineer, Applied AI

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, related technical field, or equivalent practical experience.
• 10 years of experience in a customer-facing technical role (e.g., Customer Engineering, Solutions Architecture, Technical Consulting) or software engineering role working with cloud-native architectures.
• 2 years of experience building, prototyping, or deploying Generative AI, Conversational AI, or Enterprise Search/Information Retrieval solutions using AI APIs, models, or developer frameworks.
• Experience writing and debugging code in Python or a modern backend language, including experience working with REST APIs, asynchronous operations, and OpenAPI specifications.
• Experience engaging with, presenting architectures to, and leading technical discussions with enterprise technical stakeholders (e.g., Enterprise Architects, Engineering Leads).

Preferred qualifications:

• Experience developing autonomous agents using frameworks like Google ADK, LangGraph, and Semantic Kernel.
• Expertise in enterprise RAG optimization, hybrid vector retrieval, semantic search, ranking engines, and Vertex AI Search.
• Deep knowledge of tool execution patterns, MCP, OpenAPI schemas, and connecting agents to CRMs, ERPs, and e-commerce APIs.
• Versed in cloud security perimeters (IAM, VPC, CMEK), Terraform infrastructure, distributed tracing, and hallucination evaluation.
• Skilled in building agentic shopping workflows integrated with product catalogs, dynamic pricing, and order management backends.

About the job:

As a Customer Engineer, Applied AI, you are the technical authority and trusted advisor helping our most strategic enterprise customers design, validate, and scale next-generation AI agents on Google Cloud. You sit at the intersection of deep engineering and go-to-market strategy—translating transformative AI capabilities (such as multimodal foundation models, multi-agent reasoning, and advanced retrieval) into concrete, high-ROI business solutions.

In this role, you will lead technical strategy across Applied AI agents, such as customer-facing experiences (including Google Enterprise Customer Experience/GECX, agentic shopping and product discovery, intelligent conversational agents, and omnichannel journeys). You will architect robust solution blueprints, build high-impact reference prototypes, tackle enterprise security and latency requirements, and partner with C-level stakeholders (CTOs, CISOs, CAIOs) to accelerate the customer journey from initial discovery to production-ready enterprise deployment.

It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.

Responsibilities:

• Design end-to-end architectures for enterprise workloads—spanning front-office customer journeys (GECX, omnichannel voice/chat), agentic commerce/shopping, back-office automation, multi-agent systems, and intelligent enterprise/product search.
• Guide customers in defining reasoning loops, tool/API execution, dynamic SOP retrieval, and advanced RAG, build functional prototypes validating critical Customer User Journeys (CUJs), search/discovery flows, and multimodal extraction.
• Architect compliant solutions adhering to VPC-SC, IAM, CMEK, data residency, and AI safety boundaries, integrating Google Cloud AI with existing CRMs, CCaaS/telephony platforms, e-commerce backends, and ERPs.
• Define technical blueprints ensuring seamless transitions to FDE, PSO, and partner teams; relay field feedback to Product Engineering and create reusable solution accelerators, whitepapers, and playbooks.