Principal Machine Learning Engineer I
About The Position
LexisNexis Legal & Professional, part of RELX, is a global provider of information-based analytics and decision tools. The company has a long history of using AI and advanced technologies in the legal market to enhance productivity and transform legal practices. They are developing ethical and powerful generative AI solutions using a flexible, multi-model approach, prioritizing the best model for each legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop and validate solutions in line with RELX Responsible AI Principles. This role is for a Principal Machine Learning Engineer who will design, build, and operate scalable AI/ML systems and agentic architectures for next-generation legal research and analytics products. The position requires a blend of deep ML expertise, distributed systems engineering, and AI platform development. The engineer will be instrumental in creating enterprise-grade AI systems, including LLM infrastructure, RAG pipelines, and autonomous agent frameworks for complex, large unstructured data.
Requirements
- 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
Responsibilities
- 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.
Benefits
- Annual incentive bonus
- Country specific benefits
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What This Job Offers
Job Type
Full-time
Career Level
Principal
