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# Principal Machine Learning Engineer

- **Company:** LexisNexis
- **Location:** Raleigh, NC, United States
- **Experience:** Expert
- **Salary:** $136,100.0 - $252,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Automated Storage and Retrieval Systems, Continuous Delivery, Continuous Integration, Data Governance, Distributed Systems, Machine Learning, Language Modeling, Systems Development Life Cycle, Software Engineering, Systems Architecture, System Testing, Unstructured Data, Lexis, Large Language Models, Information Technology, Machine Learning Operations
- **Published:** August 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/principal-machine-learning-engineer-i-raleigh-nc--270d056e-f311-47b1-85eb-ff33124b4774

## About the Role

* 10 + years of Machine Learning/Software Engineer experience
 * Master's degree or bachelor's degree, computer science degree is highly desirable.
 * Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data
 * Experience with ML deployment to production, Architectural Design, Architectural Services, Artificial Intelligence (AI), Attorney, Best Practices, Business Practices, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Data Science, Distributed Computing, Establish Priorities, Genetics, Legal, Legal Research, LexisNexis, Machine Learning, Maintain Compliance, Modeling Languages, Performance Reviews, Problem Solving Skills, Productivity Management, Quality Metrics, Scalable System Development, Software Engineering, System Architecture, System Test, Systems Engineering, Test Design, Test Plan/Schedule, Traceability, Unstructured Data, Use Cases

## Description

Do you love collaborating with teams to solve complex technical problems?, * Provide architectural direction and code-level guidance.

 * Establish engineering best practices for ML system design, testing, and deployment.
 * Conduct design reviews, performance reviews, and technical roadmap planning.
 * Architect distributed ML systems serving multiple global products.

 * Standardize infrastructure patterns for LLM serving and retrieval systems.
 * Define and implement enterprise-ready agentic frameworks.
 * Architect multi-step reasoning systems.
 * Lead decisions on deterministic workflows vs. autonomous agents.
 * Implement guardrails, safety layers, and traceability mechanisms.
 * Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.
 * Establish CI/CD standards for ML lifecycle management.
 * Ensure compliance with enterprise data governance and responsible AI standards.


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