AI Machine Learning Scientist

About the role

Anticipated End Date: 2026-07-15 Position Title: AI Machine Learning Scientist Job Description: AI Machine Learning Scientist Location: This role requires associates to be in-office 1 day 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. Ideal candidates will be able to report to one of our Pulse Point locations in Indianapolis, IN, Atlanta, GA, Tampa, FL, or Richmond, VA. 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 AI Machine Learning Scientist is responsible for Artificial Intelligence (AI) scientific and statistical methods to assist with product creation, development and improvement. 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. Help build reusable AI capabilities, evaluation frameworks, and governance processes that ensure AI systems are reliable, measurable, compliant, and aligned with Responsible AI principles. Will work closely with engineering, product, data science, and business teams to translate complex business challenges into production-ready AI solutions. How you will make an impact: Design, develop, and deploy AI/ML and Generative AI solutions that address business and operational challenges at enterprise scale. Build and maintain infrastructure, pipelines, and services that connect structured and unstructured data sources for AI-driven applications. 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. Collaborate with engineering and product teams to integrate AI capabilities into enterprise platforms and applications. 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. Support experimentation, benchmarking, and model comparison activities to improve decision-making and accelerate AI innovation. Partner with cross-functional stakeholders to translate business requirements into production-ready AI capabilities and services. Contribute to technical standards, architecture decisions, and best practices for enterprise AI engineering. Develop experimental and analytic plans for machine learning algorithms and data modeling processes, use of strong baselines, and ability to accurately determine cause and effect relations. Minimum 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. Preferred Skills, Capabilities and Experiences: Experience building and deploying LLM- or SLM-based applications in production environments highly preferred. Experience developing Retrieval-Augmented Generation (RAG) systems, semantic search, vector databases, embeddings, and prompt engineering techniques highly preferred. Experience designing and implementing AI agents, tool-calling workflows, or agentic architectures highly preferred. Experience evaluating AI systems using automated evaluation frameworks, benchmarking approaches, and human-in-the-loop review processes highly preferred. Experience building scalable AI/ML pipelines and services using cloud-native architectures highly preferred. Experience with MLOps practices including CI/CD, model deployment, monitoring, observability, drift detection, and lifecycle management highly preferred. Experience with Python and modern AI/ML frameworks and libraries (e.g., PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex, Hugging Face, or equivalent) highly preferred. Familiarity with Responsible AI principles, model governance, bias testing, explainability, and auditability requirements highly preferred. Experience integrating AI solutions with APIs, enterprise platforms, and

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at Elevance Health (Anthem)? Share your experience