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AI Infrastructure Engineer
Sr. Cloud AI & ML Engineer
Minimum Qualifications
- Bachelor’s degree in Computer Science or equivalent practical experience
- 3 years of experience building machine learning solutions and working with technical customers
- Experience designing cloud enterprise solutions and supporting customer projects to completion
- Experience coding in one or more general-purpose languages (e.g., Python, Java, Go, C or C++), including data structures, algorithms, and software design
Preferred Qualifications
- Experience working with recommender systems, data pipelines, or distributed machine learning
- Experience with deep-learning frameworks (e.g., TensorFlow, XGBoost)
- Understanding of production machine-learning concerns
- Knowledge of data warehousing concepts, including:
- Technical architectures
- Infrastructure components
- ETL/ELT processes
- Reporting/analytic tools (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce)
About the Role
The Google Cloud Consulting Professional Services team helps customers transform their business by leveraging Google’s global network, cloud infrastructure, and AI-driven solutions. As a Cloud AI Engineer, you’ll work closely with customers—including Google’s largest and most innovative enterprises—to design and implement machine learning solutions using Google’s core AI products, including TensorFlow, DataFlow, and Vertex AI.
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Your responsibilities include:
- Acting as a trusted AI/ML advisor, solving complex business and technical challenges
- Mentoring customers on optimizing feature extraction, data validation, model monitoring, and production pipelines
- Collaborating with clients, partners, and Google product teams to deploy production-ready AI solutions
- Developing best practices, tutorials, blog posts, and reference architectures
You’ll also spend up to 30% of your time traveling for onsite engagements—whether leading workshops, conducting technical reviews, or facilitating deployments.
Responsibilities
- Be a trusted technical advisor,helping customers solve complex ML challenges in production
- Coach customers on feature engineering, data governance, observability, and governance for enterprise AI/ML systems
- Work with partners, customers, and internal product teams to define tailored AI solutions that drive measurable outcomes
- Create and share best practices, documentation, and tutorial content to empower the wider Cloud community
- Travel globally (up to 30%) for client-facing work, including workshops, migrations, and product engagements
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About Google & Equal Opportunity
Google is an equal opportunity employer. We’re committed to creating an inclusive environment where employees can thrive and feel valued. Dedicated to diversity, equity, and accessibility, we reserve the right to extend offers based on aptitude and potential without regard to individuals’ protected characteristics.
For candidates with disabilities or special needs requiring accommodation, please visit [Google’s EEO & Application Accommodations](insert link note once actual process is referenced). If you’d like to discuss your candidacy, your direct supervisor—or representative—would be happy to arrange a conversation.
Why It Matters
Google Cloud powers enterprise innovation in over 200 countries. Whether building ** andenly-ready AI models, optimizing workflows, or cross-training customers**, you’ll partner with companies seeking to solve the world’s biggest challenges—while growing with one of the tech industry’s most dynamic teams. Cloud + AI = Limitless.
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