Technical Solutions Engineer, Cloud Data and AI

Work with customers on their ML deployments to resolve issues and achieve production readiness and availability. Partner with Product and Engineering teams to improve products based on customer feedback.
Manage customer problems through effective diagnosis, resolution, documentation/implementation of investigation tools to increase productivity for customer issues on Google Cloud Platform products.
Develop an in-depth understanding of Google Cloud’s Artificial Intelligence/Machine Learning (AI/ML) products/solutions and underlying architectures by troubleshooting, reproducing, and determining the root cause for customer-reported issues, building tools for faster diagnosis.
Act as consultant and subject matter expert for internal stakeholders in engineering, sales, customer organizations to resolve technical deployment obstacles and improve Google Cloud.
Work as part of team of engineers/consultants that globally ensure 24-hour customer support, including working non-standard work hours/shifts, and may include weekend work.

Bachelor’s degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
2 years of experience programming in a general purpose coding language or in system design, and troubleshooting and advocating for customers' needs, and triaging technical issues.
2 years of experience with 2 or more of the following: Web Tech, Data/Big Data, Systems Admin, Machine Learning, Networking, Kubernetes.
Experience in Computer Networking (e.g., Firewalls, Routing, Load balancing, etc.), web technologies (e.g., HTTP, HTML, DNS, TCP, etc.), and artificial intelligence (AI) concepts and techniques.

2 years of experience in recommendation systems, natural language processing, speech recognition, or computer vision.
Experience troubleshooting ML models (e.g., Tensorflow, Keras, PyTorch).
Experience working with public cloud services and infrastructure and working with networking/peering with private cloud.
Experience with performance analysis of containerized systems and good understanding of underlying resources like Kubernetes, compute resources.
Excellent leadership and influencing skills in AI/ML application, with the ability to lead the design and implementation of AI-based solutions, web services, debugging tools.

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