Our client is seeking a machine learning engineer to usher in their own information revolution. The software and technology you’ll use to do that – Databricks, AWS, and MLflow, to name a few, serve the purpose to make information and data available and useful to employees and customers on a scale yet unseen. With your experience and skills in automating the deployment of data science models and building repeatable pipelines, you’ll help address complex issues like worker safety, fraud, and the evolving customer experience. You’ll work alongside data scientists, architects, and engineers to empower data-driven decision-making on behalf of customers, real people across Oregon who depend on the service every day.  If you’d like to help shape the contours of what’s taking shape for this analytics and data group, as well as work at a flexible, mission-driven, community-oriented company, apply for this new machine learning engineer.  We value diversity in the workplace and encourage women, minorities, and veterans to apply.

Type: Perm

Location: Remote 

Responsibilities

  • Recommend and implement the operationalization of data science / machine learning / AI analytics solutions using expertise in cloud architecture and MLOps.
  • Take part in the entire model lifecycle and pipeline building from requirements development to deployment.
  • Partner with data scientists, analytics solutions architects, and data engineers to support the model pipeline, including data ETL, data lakes, data catalogs, data labeling systems, model training, model deployment & inference, algorithm orchestration, model monitoring, and model update/retraining.
  • Develop end-to-end Data/Dev/MLOps pipelines based on in-depth understanding of model lifecycle to ensure analytics solutions are delivered rapidly, efficiently, predictably, and sustainably.
  • Support lifecycle management of deployed ML apps life cycle management (e.g. new releases, change management, monitoring, and troubleshooting).
  • Enhance and improve the code deployment and model monitoring frameworks and project operations documentation.
  • Knowledge and experience with MLOps as a practice, including experience in MLOps using at least one of the popular frameworks or platforms (e.g., Databricks, MLFlow, AWS Sagemaker). Minimum 4 years of design and production automation (build, validate, deploy, test automation) end-to-end automated data and ML pipelines, including within cloud webservices (AWS, Azure, Databricks), data lake platforms, and machine learning platforms.
  • Minimum 4 years of experience working in an AI / ML / data science context alongside Data Scientists and/or ML Engineers
  • Demonstrated experience with data ETL/ELT and processing technologies such as SSIS, Apache Airflow, Apache Spark, and Fivetran.

Qualifications

The following qualifications are recommended:

  • Knowledge and experience with MLOps as a practice, including experience in MLOps using at least one of the popular frameworks or platforms (e.g., Databricks, MLFlow, AWS Sagemaker). Minimum 4 years of design and production automation (build, validate, deploy, test automation) end-to-end automated data and ML pipelines, including within cloud webservices (AWS, Azure, Databricks), data lake platforms, and machine learning platforms.
  • Minimum 4 years of experience working in an AI / ML / data science context alongside Data Scientists and/or ML Engineers
  • Demonstrated experience with data ETL/ELT and processing technologies such as SSIS, Apache Airflow, Apache Spark, and Fivetran. 
  • Education: Bachelor’s degree, or equivalent combination of education and experience, in Computer Science, Engineering, Mathematics, or a related field. Masters or PhD in Computer Science, Physics, Engineering, or Math or a Professional Data Management Certification are preferred.