Google Inc.
Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure.
As a Cloud AI Developer, 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.
Responsibilities
Be a trusted technical advisor to customers and solve complex machine learning challenges. Coach customers on the practical challenges in machine learning systems: feature extraction and feature definition, data validation, monitoring, and management of features and models. Work with customers, partners, and Google product teams to deliver tailored solutions into production. Create and deliver best practice recommendations, tutorials, blog articles, and sample code. Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities. Qualifications
Bachelor's degree in Computer Science or equivalent practical experience. 6 years of experience building machine learning solutions and working with technical customers. Experience designing cloud enterprise solutions and supporting customer projects to completion. Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design. Preferred Qualifications
Experience working with recommendation engines, data pipelines, or distributed machine learning. Experience with deep learning frameworks (e.g., Tensorflow, pyTorch, XGBoost). Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume). A solid understanding of the auxiliary practical concerns in production machine learning systems. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law.
#J-18808-Ljbffr
Be a trusted technical advisor to customers and solve complex machine learning challenges. Coach customers on the practical challenges in machine learning systems: feature extraction and feature definition, data validation, monitoring, and management of features and models. Work with customers, partners, and Google product teams to deliver tailored solutions into production. Create and deliver best practice recommendations, tutorials, blog articles, and sample code. Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities. Qualifications
Bachelor's degree in Computer Science or equivalent practical experience. 6 years of experience building machine learning solutions and working with technical customers. Experience designing cloud enterprise solutions and supporting customer projects to completion. Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design. Preferred Qualifications
Experience working with recommendation engines, data pipelines, or distributed machine learning. Experience with deep learning frameworks (e.g., Tensorflow, pyTorch, XGBoost). Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume). A solid understanding of the auxiliary practical concerns in production machine learning systems. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law.
#J-18808-Ljbffr