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 an Applied AI Customer Engineer, you will accelerate Google Cloud’s success by providing technical expertise in Conversational Artificial Intelligence and customer experience. In this role, you will bridge the gap between business issues and highly technical Generative AI solutions. You will partner with Sales teams on high-stakes engagements as a subject matter expert to differentiate Google Cloud, and will be the lead architect for advanced Conversational AI frameworks to design resilient and scalable AI solutions. You will help customers design architectures and solve customer experience issues using first-party Generative AI solutions. You will be a technical expert and a thought leader who will help enterprises navigate Conversational Artificial Intelligence and customer experience from proof-of-concept to production.
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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 20% bonus target + bonus + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as a trusted advisor to prospective and existing customers, explaining technical features and designing cloud-based architectures.
- Provide technical guidance on integrating AI solutions with existing enterprise data stacks and third-party stacks such as CRM, etc.
- Lead the rapid development of Proof-of-Concepts (PoCs) and Minimum Viable Products (MVPs), demonstrating the practical application of Google Cloud solutions, troubleshoot technical roadblocks, and recommend integration strategies for end-to-end Google Cloud solutions.
- Collaborate with product management to prioritize solutions that drive customer adoption and share in-depth AI expertise through product briefings and technology advocacy.
- Present the business value of Applied AI solutions to executive leaders and represent Google Cloud at conferences and industry events.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
- 10 years of experience with designing cloud-native enterprise-grade technical architecture in customer-facing or support roles.
- 1 year of experience with Conversational AI technologies, including designing conversational flows/agents and operating Speech-to-Text, Text-to-Speech (STT/TTS).
- Experience building or leveraging AI solutions, ML APIs, prompting, agent tooling, eval frameworks, and modern AI frameworks, and embedding into demos.
- Experience engaging with and presenting to both technical stakeholders and executive leadership.
Preferred qualifications:
- Experience with building conversational applications and integrating it with third-party tooling (e.g., CRM, ticketing, telephony platforms).
- Experience coding in Java, C++, or Python and vibe coding.
- Familiarity with large language models (LLMs), retrieval-augmented generation (RAG), machine learning templates, and document/image AI.
- Understanding of modern development methodologies and application performance tuning.