About the opportunity

The Machine Learning Infrastructure Engineer will be responsible for improving existing and creating new machine learning platform capabilities for Data and ML initiatives at Ontra. You will be contributing to solve problems with a mindset toward rapid iteration to deliver automated, repeatable solutions for product and building frameworks to boost effectiveness of Data and ML teams.

This is a great opportunity for someone who wants to work on a growing data platform and is excited about the opportunity to contribute to it. If you enjoy creating new tooling around automation and data processing, laying down foundational ML infrastructure with efficiency in mind, building influential platform capabilities and microservices, then this role is for you.

The ML Infrastructure Engineer will report directly to the Director of Data Infrastructure, and the location is flexible.*

What you’ll do

• Machine Learning: Contribute machine learning tooling and architecture that improves workflows of data practitioners (data engineering, ML, and analytics)
• Modeling: Develop, implement, and maintain model serving and monitoring solutions to support key product features and initiatives.
• Deployment: Enhance the team’s delivery capabilities by deploying and orchestrating cloud-based software and infrastructure in a scalable manner.
• Platform Support: Support ML and Analytics teams uncover insights and enable rapid development for solutions to legal tech problems.
• Data Governance: Champion efforts to enforce data governance, quality and security across the organization

What you’ll bring

• Experience: 3-5+ years of experience as an ML Infrastructure Engineer
• Engineering: Fluency in Python and proven ability to ship flexible, maintainable code. Ships well-tested and robust code, guided by solid engineering principles.
• Cloud Infrastructure: Comfort using AWS tooling and experience maintaining modern data and ML infrastructure including but not limited to: infra-as-code frameworks, job scheduling, application load balancers / microservices, warehousing, container repositories).
• Machine Learning: Demonstrates foundational understanding of the ML lifecycle, including feature & model stores. Experience developing feature engineering pipelines, tooling and frameworks for ML teams, model serving architectures. Support deployment, testing, and monitoring of models in a production system. NLP & Sagemaker is a plus.
• Pipelines: Experience developing and productionizing complex data processing pipelines. Event logs and real-time data streaming are a plus.
• Data: Experience with data modeling, experimentation, and split testing 
• Communication: Excellent communication and problem solving skills and ability to effectively collaborate with technical and business partners
• Best-in-class methodology: Familiarity with data governance frameworks, security, and Agile methodology