
Cloud AI Engineer, Professional Services, Google Cloud
- São Paulo - SP
- Permanente
- Período integral
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mexico City, CDMX, Mexico; Buenos Aires, Argentina; São Paulo, State of São Paulo, Brazil; Santiago, Chile.Minimum 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.
- Experience building and deploying custom ML models into production, including experience with deep learning frameworks and with designing and deploying agentic AI workflows for business process automation.
- Ability to communicate in English fluently as this is a customer-facing role.
- 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).
- Understanding of the auxiliary practical concerns in production machine learning systems.
- 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% in-region for meetings, technical reviews, and onsite delivery activities.