Cloud AI Engineer, Google Cloud

Key Skills

Aartificial intelligence, Algorithms, Apache Hadoop, Apache Hive, Apache Pig, C Programming, C++, Data Analytics, Data Warehousing, Deep Learning, ETL frameworks, Google Cloud Platform (GCP), Java Programming, Machine learning techniques, Natural Language Processing (NLP), Perl Programming, Python Programming, PyTorch, SPARK Programming, Statistics, TensorFlow, XGBoost

Job Description

Minimum qualifications:

• Bachelor's degree in Computer Science, Software Engineering or other Quantitative field (e.g. Physics, Mathematics, Statistics) or equivalent practical experience.
• Experience with Machine Learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning or natural language processing.
• Experience in coding and scripting in one or more languages (Python, Perl, C, C++ or Java).

Preferred qualifications:

• Experience with deep learning frameworks (such as TensorFlow, PyTorch, XGBoost).
• Experience in delivering complex Artificial Intelligence (AI) or data analytics projects.
• Experience in customer-facing technical consulting roles.
• Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, Extract, transform, and load (ETL)) and reporting/analytic tools and environments such as Apache Beam, Hadoop, Spark, Pig, Hive.
• Demonstrated ability to translate business problems in AI projects.

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 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 will support customer implementation of Google Cloud products through architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, and monitoring. You will also have to travel up to 30% of the time, although we also frequently use video conferencing in-region for meetings, technical reviews, and onsite delivery activities.

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.