Cloud Machine Learning Operations Engineer, Delivery Center

Key Skills

Java Programming, Python Programming, MongoDB, C++, SQL, Big Data Technology, Machine learning techniques, ETL/ELT technologies, NoSQL, TensorFlow, SparkML

Job Description

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, or equivalent practical experience.
• Experience managing client-facing projects, troubleshooting technical issues, and working with engineering, sales, and services teams.
• Experience with Machine Learning, MLops, data augmentation, hyperparameters tuning, error analysis, and performance auditing.
• Experience writing software in one or more languages (e.g., Python, Java, C++) and with SQL.

Preferred qualifications:

• Experience in Big Data, information retrieval, data mining, or machine learning.
• Experience building multi-tier high availability applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, TensorFlow, etc.).
• Experience working with and/or on data warehouses (i.e., data warehouse technical architectures, infrastructure components, ETL/ ELT, reporting/analytic tools and environments, and data structures).
• Experience architecting and developing software or Internet scale production-grade Big Data solutions, in virtualized environments such as Google Cloud Platform or other similar platforms.

About the job

As a Cloud Machine Learning Operations (MLOps) Engineer, you will help customers to operationalize ML models on the Google Cloud Platform. You will work on migrations and transformational projects, to optimize for scale, and troubleshoot potential issues. In this role, you will work with customers/partners to build ML solutions and support ML operations by orchestrating experiment steps, modularizing experiment’s code to pipelines and components, ensuring models CT and CD, and providing data and model validation features integrated with monitoring and alerting. You will deliver workshops to educate and empower customers and partners. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.

Responsibilities

• Work with customers to operationalize ML solutions.
• Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
• Travel when required (up to 15%).