ABOUT THIS FEATURED OPPORTUNITY
We’re seeking a machine learning engineer to usher in our own information revolution. The software and technology you’ll use to do that – Databricks, AWS, and MLflow, to name a few – are slightly more advanced than the oil-based ink Gutenberg used, but they’ll serve the same purpose: making information and data available and useful to our client's 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 us 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 our customers, real people across Oregon who depend on us every day.
THE OPPORTUNITY FOR YOU
- 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.
- Other responsibilities as required
KEY SUCCESS FACTORS
- 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.
- Other combinations of skills and experience may be considered.