Minimum qualifications:

　　Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.

　　4 years of experience working in AI/ML as a technical sales engineer or in software engineering.

　　Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch).

　　Experience with Generative AI as a user or a developer.

　　Experience delivering technical presentations and leading detailed business value sessions.

　　Preferred qualifications: Certification in GCP Professional Machine Learning Engineering.

　　Experience architecting MLOps systems in enterprise environments and building, scaling, and optimizing enterprise-grade Machine Learning systems.

　　Experience in working with batch and online model serving, with the understanding of model management and monitoring.

　　Knowledge of GCP services and how to use them for analytics and data engineering, including BigQuery and Vertex AI.

　　Knowledge of Vertex AI model deployment and Vertex Pipelines for automation.

　　Excellent infrastructure building and maintenance skills on the GCP for data engineering pipelines.

　　About the job

　　*Use this template only when existing Job Description options don't apply. For one-off use cases, not ongoing roles. If you need to create a NEW template for your team submit a request at go/jdreview.*

　　As an AI/ML Field Solutions Architect, you will support Google Cloud sales teams and engineering incubate, pilot, and deploy Google Cloud's industry leading AI/ML and Generative AI technology at AI natives and innovators, large enterprises, and early stage AI startups. You will help customers innovate faster with state of the art solutions using Google Cloud's flexible and open infrastructure including AI Accelerators (TPU/GPU).

　　In this role, you will identify, assess, and develop Generative AI and AI/ML applications by applying key industry tools, techniques, and methodologies to solve problems. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. Along the way, you will work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings. You will navigate ambiguity, troubleshoot and find solutions, and learn quickly in a rapidly changing technology space.

　　Google Cloud accelerates organizations' ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology - all on the cleanest cloud in the industry. 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

　　Be a trusted advisor to our customers by understanding the customer's business process and objectives. Architect AI-drive, spanning Data, AI, and Infrastructure, and work with peers to include the full cloud stack into overall architecture.

　　Demonstrate how Google Cloud is differentiated by working with customers demonstrating features, tuning models, optimizing model performance, profiling, and benchmarking. Troubleshoot and find solutions to issues training/serving models in a large-scale environment.

　　Build repeatable technical assets such as scripts, templates, reference architectures to enable other customers and internal teams. Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.

　　Coordinate regional field enablement with leadership and work closely with product and partner organizations on external enablement activities. Travel as needed.