Principal Machine Learning Engineer

Overview

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At Regions, the Principal Machine Learning Engineer (MLE) supports the Data and Analytics organization by designing, customizing, and implementing data science and analytics platforms for the development and production of machine learning models. The MLE will use machine learning knowledge and software architecture expertise to design model promotion pipelines, implement dev/ops capabilities for machine learning models, and design processes for ensuring provenance across training and inference.

The Principal MLE is a forward thinking and visionary role and will be a key leader in developing and supporting Regions’ model lifecycle infrastructure strategy.

• Designs and implements self-service model deployment strategies
• Promotes Regions’ cloud strategy and designs cloud-native machine learning workflows
• Develops tooling to facilitate model development, deployment, and monitoring of data products
• Develops automated workflows for machine learning pipelines
• Collaborates with data engineers and data scientists to develop data and model pipelines
• Creates RESTful application programming interface (APIs) for streamlining, monitoring, and reporting on the model lifecycle
• Designs and implements deployment infrastructure
• Creates and evangelizes best practices in model operations
• Helps contribute to a collaborative, open developer environment
• Leads improvements in methodology or initiatives to address capability gaps or increase efficiency
• Offers advice and guidance to junior associates for the sake of continuous improvement

This position is exempt from timekeeping requirements under the Fair Labor Standards Act and is not eligible for overtime pay.

This position is incentive eligible.

• Bachelor's degree in Computer Science or a quantitative field
• Eight (8) years of related experience

• Master's degree
• Experience with big data and machine learning tools such as Spark, Dask, Kubeflow, Airflow
• Experience with micro-service architecture and web-services
• Experience with cloud technologies such as AWS, GCP, Azure, Snowflake, Terraform
• Working knowledge of machine learning models, common model deployment pitfalls, and inherent complexities

• A proven track record of working in teams and of leading projects
• Demonstrated experience with software engineering best practices and implementing software development life cycles
• Demonstrated success in one or more of the following programming languages:
  Python, Golang, Java, JavaScript, Rust and Scala
• Experience delivering and scaling models in production
• Experience developing RESTful APIs
• Experience with Docker/Kubernetes
• Partnering with Data Scientists, Data Engineers, AI Engineers on delivering production data, machine learning, and AI use cases
• Building re-usable ML and AI deployment pipelines
• Designing and building architecture and patterns for training, registering, deploying and monitoring models

Regions will not sponsor applicants for work visas for this position at…