Google·SaaS·11 days ago
11 days ago
Cloud AI Engineer I, Google Cloud (English)
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What you'll do
- Lead the delivery, deployment, and implementation of Gemini Enterprise solutions, utilizing Agent Development Kits (ADKs) to solve complex, enterprise-scale technical customer challenges.
- Act as a trusted technical advisor to Google’s most strategic customers to shape their AI strategy, drive, and accelerate the adoption of Gemini Enterprise.
- Provide architectural guidance on existing product challenges, collaborate closely with the engineering team to address gaps, and architect scalable workarounds for edge cases.
- Deliver leading practice recommendations and technical presentations adapted to different levels of key business and technical stakeholders (including C-suite executives) to proactively foster Gemini Enterprise adoption and enablement.
What they're looking for
- Bachelor's degree in Computer Science, related field, or equivalent practical experience.
- 3 years of experience in software engineering, cloud architecture, or technical consulting
- 2 years of experience deploying production Generative AI (GenAI) applications.
- Experience in Python and cloud computing principles (serverless, virtualization, secure networking).
- Experience building and orchestrating agents using Agent Development Kits (ADKs) or frameworks (LangChain, LlamaIndex, AutoGen).
- Ability to communicate in English fluently in order to communicate with other teams.
Nice to have
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- Relevant Google Cloud certifications, such as Professional Cloud Architect or Professional Machine Learning Engineer.
- Experience deploying enterprise GenAI platforms (Gemini Enterprise), with deep understanding of AI security, governance, and compliance standards.
- Advanced experience implementing scalable RAG architectures connected to external productivity tools and enterprise databases, leveraging Large Language Model (LLMs) to deploy enterprise-scale multimodal solutions across text, image, video, and audio.
- Experience leading engineering teams to design modern architectures (APIs, microservices) and deploy enterprise-scale cloud transformations with integrated AI models.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
As a Cloud AI Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.
In this role, you will work with key Google Cloud customers, and together with the team, you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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.
In this role, you will work with key Google Cloud customers, and together with the team, you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more.
Responsibilities:
- Lead the delivery, deployment, and implementation of Gemini Enterprise solutions, utilizing Agent Development Kits (ADKs) to solve complex, enterprise-scale technical customer challenges.
- Act as a trusted technical advisor to Google’s most strategic customers to shape their AI strategy, drive, and accelerate the adoption of Gemini Enterprise.
- Provide architectural guidance on existing product challenges, collaborate closely with the engineering team to address gaps, and architect scalable workarounds for edge cases.
- Deliver leading practice recommendations and technical presentations adapted to different levels of key business and technical stakeholders (including C-suite executives) to proactively foster Gemini Enterprise adoption and enablement.
Minimum qualifications:
- Bachelor's degree in Computer Science, related field, or equivalent practical experience.
- 3 years of experience in software engineering, cloud architecture, or technical consulting
- 2 years of experience deploying production Generative AI (GenAI) applications.
- Experience in Python and cloud computing principles (serverless, virtualization, secure networking).
- Experience building and orchestrating agents using Agent Development Kits (ADKs) or frameworks (LangChain, LlamaIndex, AutoGen).
- Ability to communicate in English fluently in order to communicate with other teams.
Preferred qualifications:
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
- Relevant Google Cloud certifications, such as Professional Cloud Architect or Professional Machine Learning Engineer.
- Experience deploying enterprise GenAI platforms (Gemini Enterprise), with deep understanding of AI security, governance, and compliance standards.
- Advanced experience implementing scalable RAG architectures connected to external productivity tools and enterprise databases, leveraging Large Language Model (LLMs) to deploy enterprise-scale multimodal solutions across text, image, video, and audio.
- Experience leading engineering teams to design modern architectures (APIs, microservices) and deploy enterprise-scale cloud transformations with integrated AI models.
SaaS
Company
GoogleSaaS
Buenos Aires, Argentina
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