Cloud Engineer I, AI/ML, Professional Services, Google Cloud
Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Nov 6, 2026. Google scores B on the Alion truth index.
About the job
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.
In this role, you will work directly with Google’s most strategic customers on critical conversational AI projects to help them transform their contact centers using Google’s Contact Center AI (CCAI) solution. You will build conversational interfaces and virtual agents for customers using core Google products such as Dialogflow and Contact Center AI. You will be an engagement lead for Google Cloud customers doing requirements gathering, program management, addressing customer feedback and delivering virtual agent solutions. You will help them develop, deploy, and manage Dialogflow and CCAI solutions in production.
You will provide technical expertise to customer engagements while working with client executives and key technical leaders to deploy these solutions via Google’s Cloud Platform. You will maintain partnership with Google’s Cloud AI Engineering teams to build and drive excellence in our products. You will have experience in developing AI solutions applying key industry tools, techniques and methodologies. You will navigate ambiguity, you are a technical expert in your field, and will deliver customer success. You will possess excellent client-facing communication and project management skills.
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
- Build virtual agent solutions on Google’s core conversational AI platforms such as Dialogflow.
- Work with customer technical leads, client executives, and partners to scope, manage and deliver successful implementations of contact center AI solutions becoming a trusted advisor to decision makers throughout the engagement.
- Codify best practices into reusable methodologies and thought leadership assets with cross-functional teams.
- Drive successful client outcomes by managing project scope, priorities, and stakeholder alignment. Influence product evolution by advocating customer needs and resolving adoption challenges.
- Architect and deploy tailored cloud virtual agent solutions for complex customer needs.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science or equivalent practical experience.
- 3 years of experience connecting telephony systems with cloud-based artificial intelligence for contact centers (e.g., Customer Experience Advanced Solutions (CXAS)/Agent Development Kit (ADK)/Gemini Enterprise for Customer Experience (GECX)/CCAI/Dialogflow/Conversational AI/Contact Center AI).
- Experience leading the deployment of AI/ML strategies across business units.
- Experience translating technical concepts into business value for executive audiences.
- Experience building and deploying production-level, cloud-based contact center AI solutions.
- Ability to communicate in English fluently to support clients.
Preferred qualifications:
- Google Cloud certification as a Professional Machine Learning Engineer or Professional Cloud Architect.
- Experience working with recommendation engines, data pipelines, or distributed machine learning.
- A solid understanding of the auxiliary practical concerns in production machine learning systems.
- Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g. Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).