Customer Engineer (US)

As a Customer Engineer, you will be a key player in driving our customers' success by translating their business needs into innovative and effective technical solutions. You will partner with sales teams to understand customer challenges, design and present compelling solutions, and guide customers through the implementation and adoption of our products and services, with a focus on Generative AI solutions on Google Cloud. This role requires a blend of technical depth, business acumen, and strong communication skills, with a specialization in applying Generative AI technologies to solve complex business problems. A Customer Engineer needs a blend of strong technical skills in cloud-native technologies, including AI / ML, and excellent communication and problem-solving abilities to assist customers with their cloud solutions.

Core Focus : Google Cloud Platform (GCP) Expertise

• Cloud-Native Proficiency : Kubernetes, containers, and serverless architectures.
• GCP Service Mastery : Deep knowledge of core GCP services (Compute Engine, Storage, SQL, BigQuery, etc.), with a strong emphasis on Vertex AI and other AI / ML offerings.
• Vertex AI : Expertise in building, deploying, and scaling machine learning models with Vertex AI, including AutoML, custom training, and pre-trained models. Understanding of Vertex AI's role in enabling generative AI solutions.
• GCP AI / ML Services : Knowledge of other relevant Google Cloud AI / ML services and APIs (e.g., Natural Language API, Vision API).
• Infrastructure Automation : IaC with Terraform or Cloud Deployment Manager.
• Cloud Solution Design : Building scalable, secure, and resilient cloud architectures, including designing AI / ML infrastructure.
• Data Handling : Big data analytics, warehousing, and ETL / ELT processes, with a focus on leveraging data for AI / ML.
• Network & Security : Comprehensive understanding of cloud networking and security principles, including security best practices for AI / ML workloads.
• Database Management : Experience with SQL and NoSQL databases, including database solutions for AI / ML applications (e.g.,vector databases).
• Experience with enterprise architecture frameworks and methodologies.

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