Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
Accountable for design, development and maintenance of Models as Service
Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences.
Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams
Delivery of critical milestones for model deployment in the AWS and GCP clouds.
Adopt and promote MLOps best practices to the Data Science community.
Proactively monitor cloud usage to drive cost-saving opportunities across cloud accounts and deployed infrastructure.

Master’s degree in related field or 5+ years of equivalent experience in a research or DevOps function
Development experience using the AWS suite of Tools, and ideally similar experience on GCP as well
Familiarity with SageMaker, Streamlit , web security and encryption, credentials and API management tools
Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
Experience building and deploying webservices in a cloud environment.
Experience building CICD pipeline using Jenkins or equivalent
Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar
Expert-level Github experience, including Github Actions
Strong object oriented development experience using Python, Java, C#
Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery
Experience in end to end model development lifecycle, from ideation through post production monitoring.
Experience with workflow automation platforms (Apache Airflow, Autosys, similar)
Experience with Solution Design and Architecture of data pipelines
Basic understanding of Data Science model development life cycle

Senior Applied AI Machine Learning Engineer – Claims Data Science

Key skills