ML/Data Infrastructure Engineer

Toronto

Summary

Looking for a ​seasoned ML​ infrastructure engineer who wants to build the next generation AI pipelines and API’s.​ ​If you are comfortable to swim in Petabytes of data to explore trends and insights and to write scalable code for event processing and ML data pipelines, come talk to us.

Responsibilities

  • Designing high-performance RESTful web services serving terabytes of data per day.

  • Responsible for designing, developing, and implementing our backend and machine learning data infrastructure -- including event-based data ingestions, stream processing, data warehouse, data pipelines, visualization, analytics and applications

  • Develop and maintain distributed data pipelines for ETL, feature engineering, and model running

  • End-to-end data processing, troubleshooting, and problem diagnosis, performance benchmark, load balance, and code reviews.

  • Experience on data management platform (DMP) is a plus

 

Qualifications

  • 5+ years of infrastructure building. Deep knowledge of Python and Elixir is a big plus

  • 4+ years of experience designing and implementing large, scalable API for over billions of requests per day.

  • 3+ years of experience with big data technologies such as Spark, Kafka, Airflow, Druid

  • 1+ years of experience with data warehouse products such as Redshift and Snowflake.

  • Production experience on Druid, ElasticSearch, Kafka, AWS Lamba, AWS SageMaker a big plus.

  • Production experience on docker/Kubernetes a big plus.

  • Good understanding of computer algorithms

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