Senior AI Engineer

Elevance Health•Atlanta, GA

4d•Hybrid

About The Position

The Senior AI Engineer develops, analyzes and models operational, economic, management, accounting and other organizational data for the Artificial Intelligence (AI) function. This role requires associates to be in-office 1-2 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance. This approach combines structured office engagement with the autonomy of virtual work, promoting a dynamic and adaptable workplace. Alternate locations may be considered if candidates reside within a commuting distance from an office. Please note that per our policy on hybrid/virtual work, candidates not within a reasonable commuting distance from the posting location(s) will not be considered for employment, unless an accommodation is granted as required by law.

Requirements

Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 6 or more years of experience; or any combination of education and experience in configuration management, which would provide an equivalent background.

Nice To Haves

Experience in Kubernetes configuration and administration preferred.
Experience working with native AWS services in an enterprise corporate environment.
Experience in at least one of the following: Python, Go, Typescript, Bash preferred.
Experience working with both traditional Machine Learning and Generative AI workflows and systems at the enterprise level, including AI Observability and monitoring preferred.
Experience in AI tooling configuration and usage is required (ie: mlFlow, Jupyter, ETL pipelines, etc.) preferred.

Responsibilities

Recommends solutions to new and complex problems.
Develops innovative strategies, quantifies the competitive performance of the organization's operations and/or markets.
Evaluates the potential impact of changes and reports on economic forecasts that affect the industry.
Collaborate with a multi-disciplinary team of clinicians, engineers, and researchers to deliver an end-to-end product: from ideation to data collection and analysis, model architecture design, development, testing, validation, and integration into the production environment.