AI Machine Learning Scientist

Elevance Health•Tampa, FL

2d•Hybrid

About The Position

The AI Machine Learning Scientist plays a critical role in enabling the responsible and scalable adoption of AI across the enterprise. This role is responsible for designing, developing, evaluating, and operationalizing AI and machine learning solutions, including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based systems. The successful candidate will help build reusable AI capabilities, evaluation frameworks, and governance processes that ensure AI systems are reliable, measurable, compliant, and aligned with Responsible AI principles. This role will work closely with engineering, product, data science, and business teams to translate complex business challenges into production-ready AI solutions.

Requirements

Requires a Bachelor’s degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent degree and 4 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 building and deploying LLM- or SLM-based applications in production environments strongly preferred.
Experience developing Retrieval-Augmented Generation (RAG) systems, semantic search, vector databases, embeddings, and prompt engineering techniques strongly preferred.
Experience designing and implementing AI agents, tool-calling workflows, or agentic architectures preferred.
Experience evaluating AI systems using automated evaluation frameworks, benchmarking approaches, and human-in-the-loop review processes preferred.
Experience building scalable AI/ML pipelines and services using cloud-native architectures preferred.
Experience with MLOps practices including CI/CD, model deployment, monitoring, observability, drift detection, and lifecycle management preferred.
Experience with Python and modern AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex, Hugging Face, or equivalent).
Familiarity with Responsible AI principles, model governance, bias testing, explainability, and auditability requirements.
Experience integrating AI solutions with APIs, enterprise platforms, and distributed systems.
Experience reviewing, testing, validating, and hardening AI-generated code and AI-assisted development workflows.
Experience supporting production AI systems, troubleshooting issues, and driving continuous improvement.
Strong communication and collaboration skills with the ability to influence technical and non-technical stakeholders.
Healthcare, regulated industry, or enterprise-scale AI experience preferred.

Responsibilities

Design, develop, and deploy AI/ML and Generative AI solutions that address business and operational challenges at enterprise scale.
Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured and unstructured data.
Develop reusable AI capabilities including RAG pipelines, vector search, semantic retrieval, prompt orchestration, and agentic workflows.
Implement evaluation frameworks and automated testing strategies to measure model quality, accuracy, bias, safety, and performance.
Establish monitoring, observability, and governance processes to ensure AI systems remain reliable and compliant in production.
Drive adoption of Responsible AI practices by implementing evaluation standards, audit-ready documentation, and model governance controls.
Optimize AI systems for scalability, latency, reliability, and cost efficiency.