Senior AI Engineer

Elevance Health•Richmond, VA

6d•Hybrid

About The Position

Senior AI Engineer Location: 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. The Senior AI Engineer will build and scale reusable AI capabilities across the enterprise, including Evaluation-as-a-Service (EaaS) and Document Intelligence pipelines. This role will define the technical foundation for reliable, governed, and scalable AI systems, enabling teams to move from one-off solutions to standardized, production-ready capabilities. In this role, you will operate at the intersection of AI engineering, platform development, and responsible AI governance, driving both technical depth and cross-team impact. How You Will Make An Impact: 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. Design and build reusable AI platform capabilities that enable scalable model development, evaluation, and deployment across the enterprise Develop and operationalize robust pipelines for experimentation, benchmarking, and model comparison to improve decision-making and reduce rework Define and standardize schemas, APIs, and workflows to drive consistency, interoperability, and reuse across AI systems Improve reliability, reproducibility, and quality through structured experimentation, evaluation frameworks, and tracking mechanisms Partner with cross-functional teams (engineering, data science, product) to translate use cases into production-ready, reusable capabilities Optimize AI systems for performance, scalability, and cost efficiency in production environments Embed Responsible AI principles by implementing governance, evaluation standards, and audit-ready processes

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 with Python, APIs and distributed systems.
Experience with ML/LLM pipelines and evaluation techniques preferred.
Experience with productionizing AI systems (monitoring, logging, scaling) preferred.
Experience designing reusable frameworks or platform capabilities.
Familiarity with Responsible AI, model evaluation, and governance practices
Knowledge of: Document Intelligence (OCR, NLP, structured extraction).
Exposure to platform engineering and familiarity with modern AI workflows and multi model systems 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.
Design and build reusable AI platform capabilities that enable scalable model development, evaluation, and deployment across the enterprise
Develop and operationalize robust pipelines for experimentation, benchmarking, and model comparison to improve decision-making and reduce rework
Define and standardize schemas, APIs, and workflows to drive consistency, interoperability, and reuse across AI systems
Improve reliability, reproducibility, and quality through structured experimentation, evaluation frameworks, and tracking mechanisms
Partner with cross-functional teams (engineering, data science, product) to translate use cases into production-ready, reusable capabilities
Optimize AI systems for performance, scalability, and cost efficiency in production environments
Embed Responsible AI principles by implementing governance, evaluation standards, and audit-ready processes