Field Solutions Architect, GenAI, Google Cloud
Job Summary
Job Description
- Bachelor's degree in CS/Data Science or equivalent experience
- 5+ years AI/ML experience (technical sales or software engineering)
- Generative AI development experience
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch)
- Fluent English and Japanese communication
- Technical presentation and business value communication skills
- GCP Professional Machine Learning Engineer certified
- MLOps system architecture experience
- Model management and monitoring expertise
- Experience with batch and online model serving
- Knowledge of Vertex AI, Vertex Pipelines, Kubeflow, or MLFlow
- Knowledge of GCP services (BigQuery, Vertex AI)
- GCP infrastructure building and maintenance skills
7 skills required
Job Details
Minimum qualifications:
- Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.
- 5 years of experience working in AI/ML as a technical sales engineer or in software engineering.
- Generative AI experience as a developer.
- Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch).
- Experience delivering technical presentations and leading detailed business value sessions.
- Ability to communicate in both English and Japanese fluently to communicate with internal/external stakeholders.
Preferred qualifications:
- GCP Professional Machine Learning Engineer certified.
- Experience architecting MLOps systems in enterprise environments and in building, scaling, and optimizing enterprise-grade machine learning systems.
- Understanding of model management and monitoring, and experience working with batch and online model serving.
- Knowledge of Vertex AI model deployment, and Vertex Pipelines, Kubeflow, or MLFlow for automation and experimentation.
- Knowledge of GCP services and how to use them for analytics and data engineering, including BigQuery and Vertex AI.
- Good infrastructure building and maintenance skills on the GCP for data engineering pipelines.
About the job
As an AI/ML Field Solutions Architect, you will support Google Cloud sales teams and engineering to incubate, pilot, and deploy Google Cloud’s industry leading AI/ML and GenAI 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 GenAI 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 would work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings. To be successful in this role, you will need to be able to navigate ambiguity, troubleshoot and find solutions, and learn quickly in a rapidly changing technology space.
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
- Be a trusted advisor to our top 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 on POCs, 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, etc. 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.
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