GCP/ML Ops Engineer

Skills

In-depth understanding of Big Data ecosystems underlying architectures to re-platform legacy systems and build integration solutions on Google Cloud platform. Experience Have a passion for new technologies continuous improvement and actively involved with the team to design new data solutions monitoring and alerting policies for data solutions. Expertise in Java Python or any scripting language SQL/NoSQL Database systems. Experience in Big Data tools and technologies such as Big Query Databricks Airflow Spark Kafka Streams Experience in test automations ensuring data quality continuous integrations build and deployment processes using Docker GitHub Jenkins and Unix/Linux shell scripts. Experience with CI/ CD tools to deploying ML services and applications to at least one major cloud platform (AWS Azure GCP) Experience building and deploying microservices as part of Machine Learning/Data Science applications. Experience in a data science or engineering role applying ML to solve business problems. Experience with best-in-class ML operations to monitor and maintain models in production and ensure top quality stable predictions with high uptime. Proven track record of developing and deploying models on Databricks platform on GCP or other cloud services like Amazon SageMaker or Azure ML. Hands-on experience in operationalizing AI/ML models particularly in the retail domain. Experience with building continuous integration and delivery pipelines for Machine Learning applications. Strong understanding of machine learning algorithms model evaluation and optimization techniques.

Description

Bachelor’s degree in Computer Science, a related field, or applicable work experience.
5+ years of overall IT and 3 + years of ML Ops experience in Databricks on AWS, Azure or GCP cloud (GCP preferred).