Senior ML Platform Architect for Enterprise AI

LexisNexis; Raleigh (NC); On-site; USD 136,100 - 252,800; Full time

Job summary

LexisNexis Legal & Professional seeks a Principal Machine Learning Engineer to design, build, and operate scalable AI/ML systems and agentic architectures for legal research and analytics products.

You will lead the development of enterprise-grade AI systems, including LLM infrastructure, RAG pipelines, and autonomous agent frameworks for large unstructured data. Strong ML and distributed systems background required.

Qualifications

• 10+ years of Machine Learning/Software Engineer experience.
• Master’s degree or bachelor’s degree, computer science degree highly desirable.
• Strong software engineering background with experience in system design and AI features for large-scale, unstructured data.

Responsibilities

• Provide architectural direction and code-level guidance for ML systems.
• Establish engineering best practices for ML design, testing, and deployment.
• Conduct design reviews, performance reviews, and technical roadmapping.
• Architect distributed ML systems serving multiple global products.
• Standardize infrastructure patterns for LLM serving and retrieval systems.
• Define and implement enterprise-ready agentic frameworks for AI workloads.
• Architect multi-step reasoning systems and deterministic vs autonomous workflows.
• Implement guardrails, safety layers, and traceability in AI systems.
• Develop evaluation frameworks to measure reasoning quality and reliability.
• Establish CI/CD standards for ML lifecycle management.
• Ensure compliance with data governance and responsible AI standards.

Skills

Machine Learning

Software Engineer

Distributed Systems

AI Platform

LLM Deployment

Education

Master’s or Bachelor’s in CS

Job description

LexisNexis Legal & Professional seeks a Principal Machine Learning Engineer to design, build, and operate scalable AI/ML systems and agentic architectures for legal research and analytics products.

You will lead the development of enterprise-grade AI systems, including LLM infrastructure, RAG pipelines, and autonomous agent frameworks for large unstructured data. Strong ML and distributed systems background required.