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AI Strategic Cloud Engineer, Google Cloud

Google

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Software Engineering, Data Science

Bengaluru, Karnataka, India

Posted on Saturday, May 27, 2023

For Pune applicants: This role will be remote until October 2022 or when the Google office in Pune opens, (whichever is earlier). After this, you will be required to work from the Google Pune office.

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Bangalore, Karnataka, India; Gurugram, Haryana, India; Hyderabad, Telangana, India; Mumbai, Maharashtra, India; Pune, Maharashtra, India.

Qualifications

Minimum qualifications:

Bachelor's degree in Computer Science, Information Systems, related technical field, or equivalent practical experience.
3 years of experience building machine learning solutions.
Experience coding in one or more languages: Python, Scala, Java, Go, or similar with competencies in data structures, algorithms, and software design.
Experience working with technical customers.

Preferred qualifications:

5 years of experience working with recommendation engines, data pipelines, or distributed machine learning.
Experience with deep learning frameworks (such as TensorFlow, PyTorch, XGBoost).
Experience in technical consulting.
Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT, and reporting/analytic tools and environments (Apache Beam, Hadoop, Spark, Hive).
Understanding of the auxiliary practical concerns in production ML systems.

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are engineered for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Cloud AI Strategic Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.

In this role, you are the Google Engineer working with Google's largest and most ambitious Cloud customers. Together with the team you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more.

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 technical advisor to customers and solve complex Machine Learning challenges.
Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
Coach customers on the practical challenges in ML systems: feature extraction/feature definition, data validation, monitoring, and management of features/models.
Travel regularly (up to 30%, although we also frequently use video conferencing) in-person for meetings, technical reviews, and onsite delivery activities.

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