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.
As a Customer Engineer, you will partner with technical Sales teams as a subject matter expert in Generative AI to differentiate Google Cloud to our customers. You will help prospective and existing customers and partners understand the power of Google Cloud, develop creative cloud solutions and architectures to solve their business issues, engage in proofs-of-concepts, and troubleshoot any technical questions and roadblocks. You will use your expertise and presentation skills to engage with customers to understand their business and technical requirements, and present practical and useful solutions on Google Cloud. You will have excellent technical, communication and organizational skills. You will partner with internal engineering stakeholders to improve products and build solutions, optimizing for results when in production and identifying innovative ways to multiply your impact and the impact of the team as a whole.
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
- Partner with the business team to identify and qualify artificial intelligence (AI) business opportunities. Act as the primary technical advisor for AI adoption, helping customers define their strategy for "Buy vs. Build" and identify high-impact use cases.
- Share in-depth generative AI and machine learning (ML) expertise to support the technical relationship with customers, including technology advocacy, supporting bid responses, product and solution briefings, work, and partnering directly with product management to prioritize solutions impacting customer adoption to Google Cloud.
- Work directly with google cloud products to demonstrate and prototype integrations in customer and partner environments.
- Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement a complete AI solution on Google Cloud.
- Direct the adoption of agentic AI systems by guiding enterprise customers in designing and implementing scalable application capabilities.
Qualifications
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 4 years of experience with cloud native architecture in a customer-facing or support role.
- 4 years of experience in one or more general purpose programming languages (e.g., Python, Java, C++, Go) with a focus on machine learning or gen AI applications.
- Experience with agentic AI frameworks (e.g., ADK, LangGraph, CrewAI, LangChain, etc.) and integrating AI APIs (e.g., Large Language Models APIs, Speech-To-Text, Text-To-Speech, Multimodal etc.).
- Ability to communicate in English and Portuguese fluently to engage with local stakeholders.
Preferred qualifications:
- Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
- Experience implementing CI/CD solutions in the context of Generative AI applications, including the design of automated evaluation frameworks, continuous integration for agents, and observability strategies for production systems.
- Experience in architecting and developing software for distributed systems.
- Experience in data and information management as it relates to big data trends and issues within businesses.
- Understanding of modern AI architectures beyond basic prompting, including advanced Retrieval-Augmented Generation (RAG) techniques, function calling, memory banks, semantic caching, guardrail mechanisms, vector search optimization, and model fine-tuning.